[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate demonstrates exceptional technical breadth and depth, particularly in Python, Golang, CI/CD, cloud, and containerized solutions, which are highly relevant. Their proven 'Fast Learner' ability and strong career trajectory into technical leadership compensate significantly for any specific tool gaps. However, the explicit requirement for 'proven experience managing Bazel build systems' is a notable missing skill. **Strengths:** - Exceptional command of Python and Golang, proven by delivering complex, production-grade automated systems. - Extensive practical experience in CI/CD, cloud environments (AWS), and containerization (Docker), reflecting an 'automation-first' engineering mindset. - A highly adaptable 'Fast Learner' with a compelling career progression into advanced technical and AI infrastructure roles. **Missing Required:** Bazel build systems management

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptionally strong match for the Senior Data Engineer role, demonstrating a robust background in building and operating complex, data-driven systems with an automation-first and infrastructure-first mindset. While specific experience with some cloud data stack components (e.g., PySpark, BigQuery) is not explicitly listed, the candidate's proven ability to self-teach and deliver sophisticated data infrastructure (LLM clusters, trading bots, ATS) strongly indicates a high capacity to quickly acquire these skills. Their career trajectory, focus on data quality, and excellent communication skills further enhance their suitability. **Strengths:** - System Design & Automation: Proven ability to design, build, and operate complex, data-driven systems from scratch with an infrastructure-first mindset. - Data Quality & Integrity: Extensive experience ensuring data reliability, validation, and reporting, which are fundamental to data engineering. - Cross-functional Communication & Leadership: Strong track record of bridging technical and business stakeholders, translating strategy into execution, and clear communication with C-level executives. **Missing Required:** PySpark, Apache Spark, dbt, BigQuery, Databricks, Redshift, Snowflake

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is a strong match due to extensive systems architecture, automation, and full-stack development experience, coupled with exceptional business-technical translation skills. While specific Oracle Cloud experience is missing, the candidate's proven 'fast learner' profile and broad cloud/infrastructure knowledge suggest rapid adaptation. **Strengths:** - Exceptional ability to bridge business requirements with technical solutions, serving as a 'Business–Technical Bridge'. - Strong hands-on experience in systems architecture, automation, and full-stack development, with a proven track record of building and deploying production systems rapidly. - Demonstrated 'Fast Learner' profile with a clear, intentional career trajectory towards advanced technical and AI infrastructure roles. **Missing Required:** Oracle Cloud Platform Experience

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is a strong match, demonstrating extensive experience in systems engineering, automation, data integrity, and reporting, which aligns well with the core responsibilities. While specific C# and VB.Net experience is missing, a fast-learner profile and inferred Python experience mitigate this gap. The candidate's career trajectory and soft skills are highly relevant. **Strengths:** - Systems Engineering & Automation Expertise - Data Integrity & Reporting Acumen - Strong Leadership & Communication Skills

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5-mini] Candidate shows strong systems-architecture and data-pipeline experience (ETL, automation, ERP data cleansing) and proven fast-learning with end-to-end delivery; however there is no evidence of hands-on AWS data services (Glue, Redshift) or Spark which are core requirements. Overall a solid technical operations match but missing specific AWS data-engineering technologies reduces fit for this Senior Data Engineer role. **Strengths:** - Proven delivery of automation-first data pipelines and inventory/procurement systems - Experience improving data quality at scale (ERP cleansing, validation rules) - Fast learner with cross-domain systems engineering and measurable impact **Missing Required:** Hands-on AWS Glue experience, Hands-on Apache Spark experience, Hands-on Amazon Redshift experience

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5-mini] Candidate shows strong, recent experience building production data pipelines, automation-first inventory systems and algorithmic trading (allowing inference of Python, ETL and database skills) plus C2 English and a clear career trajectory toward data/systems. However there is no explicit, proven hands-on experience with AWS Glue, Apache Spark or Amazon Redshift which are core requirements for the role, so the match is good but not a strong direct fit. **Strengths:** - Proven delivery of operational data systems (inventory→procurement automation) - Strong data integrity and ERP data cleansing experience with measurable impact - Rapid self-learning and building production tooling (scrapers, trading strategies, system architecture) **Missing Required:** Proven experience with AWS Glue, Proven experience with Apache Spark, Proven experience with Amazon Redshift / columnar DW on AWS

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: Sonnet4] Candidate has strong systems architecture and automation experience with some transferable technical skills (SQL, cloud, scripting), but lacks core required technologies (C#/.NET, React.js) and airline domain experience. Fast learner profile and infrastructure background provide some mitigation. **Critical Gaps:** - Frontend development (React.js) - candidate is backend/infrastructure focused - Microsoft tech stack (C#/.NET) - candidate primarily uses Python/Go **Strengths:** - Strong SQL and database experience - Proven cloud infrastructure skills - Systems architecture and automation expertise **Missing Required:** C# and .NET Framework, React.js and JavaScript frontend, SQL Server specific experience, Azure cloud services

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: Sonnet4.5] Good technical foundation with strong infrastructure, cloud, and scripting skills that transfer well to data engineering. Candidate demonstrates fast-learning ability (self-taught systems, AI clusters, full-stack platform in weeks) and has built production data pipelines for e-commerce automation. However, missing direct experience with core required tools (Snowflake, Looker, Terraform) and lacks formal data engineering role history, though underlying skills are present. **Strengths:** - Strong AWS cloud experience with hands-on infrastructure building (self-hosted AI clusters, on-premise, Linode) - Proven data pipeline and ETL work: built automated stock-to-procurement workflow using demand history and lead times - SQL proficiency demonstrated across multiple databases (MySQL, SQLite, PostgreSQL) and ERP data quality work (TOTVS) **Missing Required:** Snowflake administration experience, Looker dashboard creation, Terraform infrastructure-as-code

[ANALYSIS_REPORT] **POOR_FIT** [Copilot: Sonnet4.5] Candidate demonstrates strong systems thinking, automation focus, and fast learning ability with proven technical project delivery. However, this role requires deep software development expertise (Python/Django, unit testing, Kubernetes) as a Senior Consultant contributor, not an operations/infrastructure role. The candidate has infrastructure and scripting experience but lacks the core software engineering depth (Django REST Framework, pytest, large-scale codebase contributions) that defines this position. **Critical Gaps:** - Senior software development role requiring deep Python/Django expertise - candidate's background is operations/systems/automation, not software engineering at scale - Core responsibility is developing and testing features for Ansible Automation Platform - candidate has infrastructure experience but not platform development experience - Expected to lead design of new features and mentor engineers on codebase contributions - candidate's technical work is automation/scripting focused, not software product development **Strengths:** - Proven fast learner with track record of self-teaching complex technical domains (built full ATS platform in weeks, self-hosted AI clusters) - Strong automation-first mindset with hands-on infrastructure experience (Docker, AWS, Linux, SQL, CI/CD pipelines) - Excellent stakeholder communication and business-to-technical translation skills with C-level experience **Missing Required:** Significant experience with Django and Django Rest Framework, Python unit and integration testing (pytest), Experience with container platforms like Kubernetes/OpenShift, CI/CD systems (Jenkins, GitHub Actions), Large-scale project version control collaboration, Software design and architecture participation, Experience presenting to stakeholders in software development context

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate demonstrates extensive, high-impact experience in architecting, developing, and deploying complex automation and AI-powered systems, including self-hosted AI clusters and robust data architectures. His 'fast learner' profile and career trajectory towards AI infrastructure, coupled with a strong ability to bridge business requirements with technical execution, align exceptionally well with the role's core responsibilities. While direct demonstrable interest in EdTech isn't explicit in the resume, his strong technical foundation and adaptability suggest a high potential for success in the domain. **Strengths:** - AI Systems Architecture and Deployment - Data Architecture and Operational Efficiency - Business-Technical Translation and Stakeholder Management **Missing Required:** Demonstrable interest in educational technology

[ANALYSIS_REPORT] **GOOD_MATCH** [2.5-Flash] The candidate is a strong match for the Senior Data Integration Engineer role, demonstrating extensive experience in designing and building automation-first systems, advanced SQL skills, CI/CD proficiency, and a clear career progression towards technical and data-centric roles. Key strengths include a proactive learning approach, end-to-end project delivery, and robust data quality initiatives. However, the candidate lacks explicit experience with NoSQL databases, specific detailed documentation for integration workflows, and direct mention of data compliance. **Strengths:** - Automation-first systems design and deployment (Data Integration) - Advanced SQL and data quality management - Proven ability to learn and apply new technologies rapidly (Fast Learner) **Missing Required:** NoSQL database systems, Detailed documentation for integration workflows and system designs, Data security compliance standards

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptionally strong match, demonstrating deep practical experience in system architecture, automation, and AI infrastructure development. Their proven 'fast learner' ability, combined with a clear career progression towards AI and extensive experience in Python-driven system building, aligns perfectly with the role's requirements. They have built and operated complex systems that infer most of the required skills. **Strengths:** - Extensive experience in building and operating complex, automation-first technical systems and AI infrastructure. - Demonstrated 'fast learner' capability, rapidly acquiring and applying new, complex technical skills (AI infrastructure, algorithmic trading). - Strong ability to bridge business requirements with technical implementation, with excellent written communication skills and a clear career trajectory towards AI. **Missing Required:** Secure coding practices

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5-mini] Candidate shows strong, recent hands-on experience with production automation, legacy ERP fixes, web app delivery (Go/JavaScript), CI/CD and self-hosted AI infrastructure—matching most technical and behavioral requirements. The primary shortfall is no evidence of PickBASIC/MultiValue professional experience, which is a specific required skill. Overall profile, rapid learning history, and relevant systems experience yield a very strong match for the role. **Strengths:** - Delivered automation-first procurement and inventory systems with measurable impact (ERP + procurement automation) - Hands-on infrastructure experience (AWS/On-prem/Cloud, Docker, CI/CD, self-hosted AI clusters) - Rapid delivery and cross-functional communication (built full website/ATS, stabilized production workflows) **Missing Required:** PickBASIC / MultiValue programming experience

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptionally strong match, demonstrating extensive experience in automation, systems architecture, and CI/CD principles. While some specific tools like Jenkins, Gerrit, and multi-platform build experience are not explicitly present, the candidate's rapid learning ability and foundational technical skills suggest quick adaptation and high potential for the role. **Strengths:** - Extensive hands-on experience in automation, system building, and operational logic. - Strong foundational skills in CI/CD, infrastructure management (Docker, AWS, Linux, Python, Shell). - Proven rapid learning ability and intentional career progression towards technical and AI roles. **Missing Required:** Jenkins, Gerrit, JFrog Artifactory, Experience supporting multi-platform development environments (Android and Windows), Groovy scripting

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptional match, demonstrating extensive experience in backend development, system architecture, and automation, directly relevant to building and scaling AI-powered platforms. Their proven track record with LLM infrastructure, API design, and a proactive, ownership-driven approach makes them highly suitable for this role. All critical requirements are met either directly or through strong inference and transferable skills. **Strengths:** - AI/LLM Infrastructure & Backend Expertise - System Design & Automation - Proactive, Ownership-Driven Innovator

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate presents an exceptional match for the Cloud Solutions Developer role, demonstrating extensive full-stack development, cloud architecture, and automation expertise. Their strong track record of designing and implementing complex systems, coupled with a proven fast-learning ability and strategic leadership experience, aligns perfectly with the job's technical and advisory demands. **Strengths:** - Full-Stack Development Expertise (Go, Python, JavaScript, SQL) - Cloud Architecture & Self-Hosted Infrastructure (AWS, Linux, Docker) - Strategic Technical Leadership & Rapid Learning Capacity

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate demonstrates exceptional alignment with the Senior Software Engineer role within a Cloud team. His extensive experience in building and operating automated, scalable systems, coupled with a strong background in software architecture, cloud infrastructure (AWS, Linode, self-hosted), and modern DevOps practices (Docker, CI/CD), makes him a strong fit. His proven fast-learner capability and intentional career progression into AI-focused infrastructure further enhance his profile, aligning well with the role's emphasis on SRE, platform resilience, and modern tech. He possesses the required programming languages (Go, Python) and an engineering academic background. **Strengths:** - Proven expertise in building and operating scalable, automation-first cloud infrastructure and distributed systems. - Strong software architecture and engineering leadership skills, capable of translating business needs into technical solutions. - Exceptional learning agility and a focused career trajectory towards cutting-edge technical domains like AI and infrastructure.

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: GPT5-mini] Strong match: candidate brings production Go experience, systems architecture and self‑hosted infra (LLM clusters, AWS, Docker, CI/CD) and relevant trading/finance domain knowledge that maps well to DEX execution logic. Primary gaps are lack of explicit Rust/C++ low‑level systems work and limited evidence of microsecond‑scale low‑latency concurrency/profiling and lock‑free/zero‑copy design, which are rampable but important for the role. **Strengths:** - Production Go experience (built ATS/website quickly with Go) - Proven systems architecture and infra build (self-hosted LLM clusters, AWS/on‑prem) - Domain knowledge in trading/finance and automation (relevant to execution primitives)

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: GPT5-mini] Candidate meets all core MUST requirements (AWS, MLOps, Python, SQL) with recent, hands-on application (self-hosted LLM clusters, AI infra, CI/CD, deployments). Lacks some enterprise-specific tools and insurance domain experience which are nice-to-haves but not blocking. **Strengths:** - Proven MLOps / AI infrastructure experience (self-hosted LLM clusters, built AI clusters) - Strong cloud and deployment skills (AWS, Docker, CI/CD) with production ops focus - Systems architect + automation-first operator with solid SQL/ETL and data-quality delivery

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: Haiku] Candidate demonstrates strong systems architecture, production automation, and self-taught infrastructure skills (self-hosted AI clusters, Docker, CI/CD), which directly transfer to ML operations. However, lacks demonstrated production ML experience (model monitoring, ML frameworks like TensorFlow/PyTorch, Vertex AI), creating a significant gap for a role requiring deployment and optimization of ML models at scale. Career trajectory shows intentional progression toward AI infrastructure, making this a GOOD_MATCH with growth potential but not yet ML-specific. **Critical Gaps:** - No demonstrated production ML model deployment experience (role requires designing/deploying ML models, candidate shows no ML frameworks or model lifecycle management experience) **Strengths:** - Self-taught infrastructure builder with proven ability to deliver complex systems (LLM clusters, ATS platform, trading algorithms) without prior domain experience—demonstrates the 'fast learner' pattern that aligns with rapidly evolving ML infrastructure - Strong production operations mindset: designed monitoring, data quality controls, and reliability systems across multiple projects (ERP validation, trading monitoring, procurement automation)—core ML Ops competency - GCP adjacency: AWS hands-on experience, Docker/containerization, CI/CD pipeline familiarity, and cloud infrastructure governance provide transferable foundation for learning Vertex AI, Dataflow, and BigQuery **Missing Required:** Python experience in ML/data science context (has Python, but not demonstrated in ML frameworks), ML frameworks (TensorFlow, PyTorch, scikit-learn), GCP ML services (Vertex AI, BigQuery, Dataflow, Cloud Run), Production model monitoring experience, Feature pipeline and data infrastructure at ML scale

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptionally strong match, demonstrating extensive hands-on experience in building and operating automation-first, AI-powered systems across the full stack. His self-taught expertise in AI infrastructure and rapid system delivery, combined with a clear career progression towards technical and AI roles, aligns perfectly with the job's requirements and the 'fast-moving, high-ownership engineering culture' of TRM. **Strengths:** - Strong hands-on experience in building and operating automation-first systems, including AI infrastructure (self-hosted LLM clusters). - Demonstrated full-stack capabilities, proficiency in Python and JavaScript, and extensive experience with backend systems and databases. - Exceptional ownership, initiative, and ability to translate business needs into technical solutions, coupled with excellent communication skills. **Missing Required:** Pinecone, Redis

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT4.1] The candidate demonstrates strong experience in technical problem-solving, automation, and data integrity, with direct application of SQL (ERP, reporting), Python (automation, trading bots), and end-to-end system delivery. While there is no explicit mention of Big Data tools like Cassandra, Flink, or Kafka, the candidate's background in building data pipelines, automating workflows, and handling large-scale operational data infers adjacent skills. Monitoring/telemetry tools (Splunk, Prometheus) are not directly referenced, but the candidate's experience with operational KPIs, reporting, and system health suggests transferable capability. **Strengths:** - Technical problem-solving in production systems - Automation and data pipeline design - Advanced English and international project experience

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is a highly experienced engineer with a strong, self-directed career trajectory towards AI infrastructure and systems architecture. He demonstrates direct experience in building and deploying complex automation and AI solutions, managing CI/CD, and proficiency with relevant databases and APIs, significantly exceeding the base experience requirements. While specific AI tools and explicit Agile experience are not named, his proven fast-learning ability and operational roles strongly imply competence. **Strengths:** - AI Infrastructure & Operations (self-hosted LLM clusters, production deployment) - Systems Architecture & Automation (0-to-1 product delivery, CI/CD, complex systems) - Broad Technical & Business Acumen (full-stack capabilities, business-to-technical translation) **Missing Required:** Scaling products to hundreds of thousands of users, Cursor, OpenCode, Aider, Void, Prompt Engineering, LLM Cost Optimization, LLM Latency Management, Langfuse, Helicone, Lunary, Agile/Scrum Experience

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5.1] Strong match on cloud/Linux infrastructure, Docker, CI/CD, Python, and self-hosted AI/LLM environments, plus clear history of building production systems and automation. However, there is no explicit experience with common ML orchestration stacks (MLflow/Kubeflow/Airflow), model training frameworks (PyTorch/ONNX/scikit-learn), or Kubernetes, and most MLOps skills are inferred rather than clearly documented. Fast-learning track record and AI infrastructure projects mitigate gaps, but tool-specific MLOps and data/monitoring ecosystem experience remain missing. **Strengths:** - Solid cloud/on-prem and Linux infrastructure background with hands-on Docker and CI/CD experience, including self-hosted AI clusters - Proven ability to design and run production automation systems (e-commerce ops, ATS/job-scoring, trading logic) with clear KPIs and governance - Fast learner with a demonstrated pattern of self-teaching complex domains (AI infra, trading, ERP data quality) and translating them into working systems **Missing Required:** Hands-on experience with ML-specific orchestration tooling such as MLflow, Kubeflow, or Airflow, Explicit use of standard ML training frameworks (PyTorch, ONNX, or scikit-learn) in production workflows, Experience managing ML workloads on Kubernetes or similar container orchestration for ML, Clearly documented end-to-end process of taking ML models from experimentation into production using MLOps best practices

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: GPT5-mini] Candidate demonstrates strong, recent experience building automation-first data pipelines, production reliability, and SQL-backed systems with clear Python usage and advanced English. The only practical gap is explicit Django experience and a couple of vendor-specific RDBMS (Oracle/MS SQL), but these are mitigated by broad web/backend experience and fast-learner evidence. **Strengths:** - Proven automation-first data pipelines and procurement/fulfillment orchestration - Strong Python and broad SQL experience applied in production - Systems architecture + operationalization experience with recent cloud and CI/CD usage **Missing Required:** Django

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate demonstrates exceptional technical breadth and depth in infrastructure, automation, and systems, with a strong self-starter profile and relevant customer-facing experience. While lacking explicit Microsoft Azure proficiency, his extensive experience with AWS, Linux, on-premise, and self-hosted cloud infrastructure, coupled with a proven fast-learning ability, suggests strong potential for rapid adaptation. His career trajectory and ability to bridge business and technical needs further enhance his suitability for the role. **Critical Gaps:** - Proficient in Microsoft Azure (Azure AD, networking, storage, compute services) **Strengths:** - Extensive experience in systems architecture, automation, and operations across various platforms. - Proven ability to build and manage complex, self-hosted infrastructure and production systems. - Strong communication and stakeholder management skills, bridging technical and business requirements. **Missing Required:** Proficient in Microsoft Azure (Azure AD, networking, storage, compute services)

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate presents a compelling profile with extensive experience in automation, data governance, advanced analytical skills, and a proven track record of building complex systems, including AI infrastructure and data pipelines. While demonstrating strong inferred capabilities in Python, AI agents, and SQL databases, a critical gap exists in explicit experience with major cloud providers (AWS/GCP), which is essential for this role's focus on scalable cloud architectures and integrations. The candidate's 'fast learner' attribute and career trajectory are highly positive. **Critical Gaps:** - Cloud experience (AWS, GCP) **Strengths:** - Strong foundation in data quality, governance, and automation - Demonstrated ability to build complex, production-grade systems from scratch (e.g., AI/ML, data pipelines) - Exceptional analytical and problem-solving skills, with a 'fast learner' aptitude and clear technical career progression **Missing Required:** Cloud (AWS, GCP), FastAPI, Pydantic, NoSQL

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is a strong match, demonstrating extensive experience in data engineering, governance, and AI leadership through entrepreneurial and consulting roles. Their 'automation-first' and 'infrastructure-first' approach, coupled with self-taught expertise in LLM clusters and algorithmic trading, directly aligns with the job's focus on innovative data architectures and AI agent development. While specific tools like FastAPI and Pydantic are not explicitly mentioned, the overall capability and rapid learning ability suggest a high potential for quick adoption. **Strengths:** - Technical leadership and strategic architectural vision in data/AI. - Proven ability to design, implement, and improve data governance, quality, and automation systems. - Demonstrated expertise in building complex, production-grade systems and AI agents with a strong learning aptitude.

[ANALYSIS_REPORT] **POOR_FIT** [Copilot: Haiku] Candidate is a systems architect with strong infrastructure, automation, and technical leadership skills, but lacks any SAP, ABAP, or ERP development experience. While demonstrating rapid learning capability and operations/data expertise, the role requires enterprise SAP application development—a completely different technical domain. The missing core technical stack cannot be overcome through inference from current experience. **Critical Gaps:** - SAP application development expertise (candidate's background is infrastructure/automation, not enterprise ERP application coding) **Strengths:** - Proven rapid learning ability (built ATS system without coding background; self-taught infrastructure) - English fluency (C2 certified, exceeds B2 requirement) and Brazil-based (meets location requirement) - Strong systems thinking and process automation mindset applicable to business logic implementation **Missing Required:** ABAP development experience, SAP S/4HANA hands-on experience, RAP framework knowledge, CDS Views implementation, OData services development

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate is an exceptional match, demonstrating deep experience in system automation, data-driven analysis, and cross-functional leadership highly relevant to a GTM Engineer role. His 'fast learner' profile and career trajectory towards technical and AI roles further enhance his suitability, compensating for minor gaps in specific marketing tools. **Strengths:** - Strong Systems Thinking & Automation Expertise: Proven ability to build and optimize complex, data-driven systems from scratch (e-commerce automation, ATS-style system). - Exceptional Data-Driven Problem Solving: Demonstrated success in leveraging data for process improvement, insights, and measurable outcomes across various domains (ERP data integrity, trading strategies). - Business-Technical Acumen & Cross-functional Leadership: Ability to bridge business requirements with technical execution, comfortable communicating with executives and C-level stakeholders. **Missing Required:** Familiarity with specific data visualization tools (Looker, Tableau, Amplitude), Explicit understanding of product funnels, experimentation frameworks, and specific growth metrics (CAC, LTV, retention)

[ANALYSIS_REPORT] **POOR_FIT** [Copilot: Sonnet4.5] The candidate demonstrates strong systems thinking, automation design, and operations experience, but lacks direct hands-on experience with the core Data Engineering tech stack (AWS data services, Airflow, Redshift, Power BI). While the self-taught background and 'Fast Learner' profile suggest adaptation potential, the role requires immediate proficiency in specialized tools (Glue, MWAA, Redshift optimization, Power BI semantic modeling) that cannot be easily inferred from ERP data cleanup or e-commerce automation. The 10+ year trajectory shows operations/supply chain analytics rather than data pipeline engineering. **Critical Gaps:** - No demonstrated experience with AWS data services ecosystem (S3, Glue, Redshift, MWAA) - No Power BI experience (required for dataset publishing and optimization) - No hands-on data pipeline orchestration (Airflow) experience **Strengths:** - Strong operations and data quality mindset (4,000+ ERP record cleanup demonstrates attention to data integrity) - Proven self-teaching ability (built scraping/ATS system from scratch, algorithmic trading) - Systems thinking and automation design (demand-driven procurement system, 40-50 orders/day automation) **Missing Required:** AWS data platform experience (S3, Glue, Redshift, Airflow/MWAA), Power BI semantic modeling and DAX, Data Lakehouse/Medallion architecture implementation, Production-grade Python for data engineering, Advanced SQL for analytical workloads

[ANALYSIS_REPORT] **POOR_FIT** [2.5-Flash] The candidate, while demonstrating strong technical aptitude, automation skills, and advanced English, lacks direct experience with SAP Business Technology Platform (SAP BTP) and specific qualifications in manual software testing, QA methodologies, test planning, and defect management, which are core requirements for this specialized SAP QA Engineer role. **Critical Gaps:** - Experience with SAP Business Technology Platform (SAP BTP) - Proven experience in software testing (manual testing focus) and strong understanding of QA methodologies, test planning, and defect management **Strengths:** - Advanced English proficiency (C2 certified) and geographical alignment with the Brazil location. - Demonstrated strong technical aptitude, fast learning ability, and experience in building and automating complex systems. - Proven track record in data integrity, process optimization, and infrastructure-first reasoning through various roles. **Missing Required:** Software testing (manual testing focus), QA methodologies, Test planning, Defect management, SAP Business Technology Platform (SAP BTP), API testing (Postman, Bruno, or equivalent)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT4.1] The candidate demonstrates strong systems engineering, automation, and QA-adjacent experience, including data validation, reporting logic, and operational controls. Manual testing is implied through ERP data integrity and reporting work, and API experience is present, but there is no direct SAP BTP experience or explicit QA methodology background. Advanced English and location requirements are fully met, and the candidate's fast learning and technical breadth add value. **Strengths:** - Advanced English and location fit - Automation and data integrity experience - Fast learner with broad technical skills **Missing Required:** Direct SAP BTP experience, Explicit QA methodology and defect management

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Flash] The candidate presents a strong match, demonstrating extensive experience in developing and operating scalable, automation-first systems, aligning well with the job's focus on high-quality and scalable application development. Their diverse technical skillset, including proficiency in multiple programming languages and SQL databases, directly addresses core requirements. Furthermore, a proven track record of self-directed learning and intentional career progression into technical domains highlights a proactive and adaptive profile. **Strengths:** - Extensive experience in building and deploying automated, scalable systems (e-commerce, ATS, AI clusters). - Proficiency in multiple relevant programming languages (Python, Go, JavaScript) and SQL databases. - Demonstrated rapid learning ability and intentional career progression into technical/AI roles.

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Lite] The candidate is a strong match due to extensive automation and systems architecture experience, proven fast learning capabilities, excellent communication, and alignment with the AI focus of the team. While specific Android testing framework experience (Espresso) and explicit software testing automation methodologies are missing, their foundational skills and ability to learn quickly make them a top candidate, reaching the maximum score. **Strengths:** - Strong Automation & Systems Architecture Experience - Proven Fast Learner & Self-Taught Capabilities - Excellent Communication & Problem-Solving Skills **Missing Required:** Espresso, Android Automation Frameworks, Software Testing Automation Methodologies

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT4.1] The candidate demonstrates strong Python experience, automation, and system architecture, with direct application of APIs, data pipelines, and CI/CD. While React/Next.js is not explicitly mentioned, the candidate's rapid self-teaching and delivery of ATS-style systems suggest high adaptability. Cloud experience is inferred but not directly stated; Docker/Kubernetes are referenced via automation and infrastructure work. **Strengths:** - Python and automation expertise - Rapid self-learning and delivery - End-to-end system design and deployment **Missing Required:** Direct experience with React/Next.js UI development

[ANALYSIS_REPORT] **POOR_FIT** [2.5-Pro] The candidate has strong adjacent experience, including functional knowledge of the TOTVS ERP, systems automation, and data integrity. However, the role is for a specialized ADVPL developer, a language and framework the candidate has no demonstrated experience with, which constitutes a critical skill gap. **Critical Gaps:** - The job requires a hands-on developer in the Protheus-specific language (ADVPL), while the candidate is a systems architect and operations manager who does not code in this language. **Strengths:** - Functional knowledge of TOTVS Protheus modules (Compras, Estoque) - Proven ability to automate business processes and improve data integrity - Strong analytical and problem-solving skills in a systems context **Missing Required:** Experiência sólida em desenvolvimento ADVPL / TLPP / MVC/ PO-UI

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: GPT5] Strong Protheus/TOTVS functional and supply chain ops experience with proven data governance and reporting, plus recent automation-first systems indicating APIs/DB skills. However, the role demands solid ADVPL/TLPP/PO-UI/MVC developer experience, which is not evidenced and Protheus table-level SQL expertise appears limited and older. Location, language, and stakeholder communication align well. **Strengths:** - Protheus/TOTVS functional background with large-scale data cleanup - Supply chain and procurement automation at operational scale (~40–50 orders/day) - Clear communication and stakeholder alignment with KPI-driven execution **Missing Required:** Solid ADVPL/TLPP development experience, PO-UI/MVC framework experience within Protheus, Demonstrated domain-level SQL on Protheus tables

[ANALYSIS_REPORT] **POOR_FIT** [Copilot: Haiku] Hugo is a systems architect with strong operational/supply chain background and proven ability to build automation-first systems, but lacks direct DevOps/cloud infrastructure experience. His infrastructure mindset and self-teaching track record are assets, but critical hands-on technical gaps exist: no Azure, Kubernetes, CI/CD pipeline implementation, or Linux administration demonstrated. This is a junior cloud ops candidate applying for an intermediate DevOps role. **Critical Gaps:** - NO DEMONSTRATED CLOUD PLATFORM EXPERIENCE (required Azure, has zero cloud ops background) - NO KUBERNETES OR CONTAINERIZATION EXPERIENCE (core DevOps requirement) - NO CI/CD PIPELINE IMPLEMENTATION (mentioned as preferred, but central to DevOps role) - NO LINUX ADMINISTRATION (explicitly required; Excel/TOTVS experience doesn't transfer) **Strengths:** - Proven ability to learn complex technical domains rapidly (TOTVS ERP, algorithmic trading, ATS systems with no prior hard-coding background) - Deep operational/reliability mindset - understands systems at scale (40-50 orders/day e-commerce automation, 4000+ ERP data records sanitized) - Systems architect perspective - translates business requirements into technical execution; strong technical-commercial interface skills **Missing Required:** Azure platform expertise, Kubernetes administration, Linux system administration, CI/CD toolchain experience, Cloud infrastructure provisioning

[ANALYSIS_REPORT] **GOOD_MATCH** [2.5-Pro] The candidate possesses the core quantitative, analytical, and client-facing skills required for an M&A Analyst role, backed by direct experience in financial analysis and advanced Excel. While they lack direct experience in M&A and SBA lending, their proven fast-learning ability and foundational finance background suggest they could adapt quickly. The primary concern is their career trajectory, which is moving away from finance towards technical systems and AI, indicating a potential mismatch in long-term interest. **Strengths:** - Strong foundational skills in financial analysis and advanced Excel - Proven client-facing and consulting experience - High analytical capability and attention to detail

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5.1] The candidate has a strong engineering background (Mechatronics) with solid analytical skills, clear written communication in C2 English, and substantial experience evaluating complex systems and algorithms, which aligns well with reviewing AI-generated mechanical engineering solutions. However, their recent experience is focused on operations, finance/supply chain, and AI infrastructure rather than hands-on mechanical engineering domains like thermodynamics, fluid mechanics, and mechanical design, which reduces recency and direct-domain fit. Overall this is a good, technically capable profile with strong reasoning and writing, but with limited recent, explicit mechanical engineering practice. **Strengths:** - Strong analytical and quantitative reasoning skills with experience designing and evaluating complex algorithmic systems. - Excellent written communication and C2-level English, with a track record of clear reporting, documentation, and KPI-focused explanations. - Significant exposure to AI systems and evaluation workflows, plus systems-engineering mindset well suited to assessing reasoning quality and model solutions. **Missing Required:** Recent, explicit work applying thermodynamics and heat transfer, Recent, explicit work applying fluid mechanics, Documented experience performing mechanical design trade-off analyses, Direct professional mechanical engineering practice after graduation

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Pro] O candidato demonstra uma forte aderência aos requisitos analíticos e de resolução de problemas complexos, com experiência comprovada na manipulação e saneamento de dados em sistemas ERP e na construção de fluxos automatizados. Embora falte a menção explícita de Power BI, sua capacidade de aprendizado rápido e de construção de sistemas técnicos do zero mitiga essa lacuna. A trajetória de carreira e os projetos demonstram um alinhamento perfeito com a essência da vaga, que é transformar dados brutos em inteligência de negócios. **Strengths:** - Forte capacidade analítica para decompor problemas complexos - Experiência com extração de insights de dados operacionais (ERP TOTVS, demanda de e-commerce) - Histórico comprovado de aprendizado rápido e entrega de sistemas complexos (fast learner bonus) **Missing Required:** Power BI

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Pro] The candidate is an exceptional systems thinker with a proven track record of analyzing messy operational processes and building automated, data-driven solutions, which is the core function of this RevOps role. His deep experience in data analysis, SQL, and automation, combined with a demonstrated ability to self-teach complex technical stacks, strongly mitigates the lack of direct experience with specific tools like HubSpot or Looker Studio. **Strengths:** - Deep experience building automation and reporting systems from the ground up - Strong data analysis and modeling skills (SQL, Advanced Excel) - Proven ability to translate business needs into technical systems and manage execution

[ANALYSIS_REPORT] **WEAK_MATCH** [2.5-Pro] The candidate is a strong systems architect with proven data analysis, project management, and database skills that align with several technical requirements. However, they lack direct experience in Customer Success methodologies and, critically, have no background in the required GIS domain, making them a weaker fit for this specific role. **Critical Gaps:** - Conhecimentos em GIS (Geographic Information Systems) **Strengths:** - Data Analysis & Interpretation - End-to-End Project Management - Automation-First Systems Thinking **Missing Required:** Conceitos básicos das metodológicas de Customer Experience e Customer Success, Atendimento a clientes externos (in a formal CS capacity)

[ANALYSIS_REPORT] **DISQUALIFIED** [2.5-Pro] The candidate is disqualified due to the on-site geographic requirement (São Paulo vs. Serra Gaúcha). Beyond that, there is a critical domain gap; the candidate's strong background in systems architecture, automation, and e-commerce operations does not align with the job's core requirement of hands-on quality management within the regulated automotive or metal-mechanical industry. **Critical Gaps:** - Experiência comprovada em gestão da qualidade nos setores automotivo ou metalmecânico **Strengths:** - Forte capacidade de automação e otimização de sistemas - Experiência em garantir integridade de dados em ERP (TOTVS) - Mentalidade de 'infrastructure-first' e resolução de problemas complexos **Missing Required:** Vivência na condução de auditorias, Experiência em gestão da qualidade

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: GPT5-mini] Strong operations + automation background with demonstrable delivery of production systems and inferred infra skills (Python, CI/CD fundamentals, scripting, source control). Missing explicit hands-on experience with core DevOps platform components requested (Jenkins, Artifactory, containerization) which reduces immediate fit for a tool-heavy role. **Strengths:** - Proven delivery of automation-first production systems (inventory, e‑commerce workflows) - Data integrity and ERP remediation at scale (TOTVS) with measurable impact on operations - Strong stakeholder communication and ability to translate exec strategy into technical scopes **Missing Required:** Jenkins, Artifactory, Containerization (Docker / Kubernetes)

[ANALYSIS_REPORT] **STRONG_MATCH** [2.5-Pro] Candidate is a STRONG_MATCH based on deep expertise in systems, automation, and process optimization, which are core to this Product Operations role. While lacking direct OTT/streaming domain experience, his proven ability to rapidly master new technical domains (e.g., building AI clusters from scratch) and his 'automation-first' mindset strongly suggest he can bridge this gap quickly. **Strengths:** - Deep experience in process automation and systems building - Proven fast learner with extreme technical aptitude - Strong problem-solving and data-driven mindset **Missing Required:** Direct experience in OTT/streaming industry, Experience with app store submission processes

[ANALYSIS_REPORT] **WEAK_MATCH** [2.5-Pro] The candidate is a strong systems thinker with proven success in automation, data quality, and bridging business with technology. However, there is a critical gap in hands-on backend development (Python/JS/Go) and a lack of experience with the required LLM/ML engineering stack (APIs, CI/CD, Kubernetes, model evaluation). **Critical Gaps:** - Backend development experience in Python, JavaScript/Typescript, or Golang - Experience with modern software deployment workflows (Git, CI/CD, Kubernetes) **Strengths:** - Excellent bridge between business stakeholders and technical teams - Proven ability to design and implement automated systems from scratch - Strong experience in data quality and integrity improvement **Missing Required:** Interacting with AI models via APIs, Designing and executing experiments with LLMs, Curating corpora for LLM training

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: Sonnet4.5] Good technical match with proven self-teaching ability and relevant infrastructure experience. Strong inference of Bash/shell scripting from Docker, CI/CD, AWS, and self-hosted infrastructure work. Key gap: no direct PowerShell experience and limited evidence of expert-level Bash proficiency (more operations/DevOps than scripting specialist). Education requirement met (Engineering degree). Fast-learner profile (+7) and demonstrated LLM usage mitigate experience depth concerns. **Strengths:** - Proven LLM-assisted development experience (built ATS platform with AI tools) - Strong infrastructure and automation background (Docker, CI/CD, self-hosted AI clusters, AWS) - Bachelor's in Engineering (Mechatronics) meets degree requirement - Excellent English (C2 certified, exceeds C1 requirement) - Fast learner with track record of rapid skill acquisition in technical domains **Missing Required:** Expert-level Bash proficiency (only intermediate inferred), PowerShell scripting, 2-3+ years dedicated shell scripting experience

[ANALYSIS_REPORT] **WEAK_MATCH** [2.5-Pro] The candidate is a very strong systems architect and engineering leader with proven success in building and automating complex operational systems from the ground up. However, there is a critical gap in their experience with the core domain of this role: blockchain, tokenization, and smart contract security, which is a significant risk for a management position. **Critical Gaps:** - Lack of experience in the core Blockchain/DeFi technology domain required to lead a specialized engineering team. **Strengths:** - Proven engineering leadership and team management - Expertise in building automated, scalable backend systems - Strong background in CI/CD, Docker, and cloud infrastructure (AWS) **Missing Required:** Familiarity with blockchains such as Ethereum, 7+ years of software development experience (has ~4 years direct coding, 10+ years technical systems)