[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a high-aptitude Systems Architect with strong Python, Automation, and DevOps (Docker/Terraform) foundations essential for the role. While strictly missing specific Modern Data Stack tools (DBT, Airflow) and explicit 'Data Engineering' titles, his experience with self-hosted inventory engines and algorithmic trading (Pandas/NumPy) demonstrates the required data manipulation and system scaling capabilities. A 'Fast Learner' profile who can likely bridge the tool gap rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a Senior/Architect-level engineer significantly overqualified for a 'Junior' Data Engineer role. Possesses exceptional core engineering skills (Python, APIs, Automation, System Design) that easily transfer. The primary gap is the specific 'MarTech' domain stack (GA4, Appsflyer), but given his history of mastering complex domains (Algo Trading, LLMs) and architecture, he will likely close this gap very quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is technically overqualified (Python/Engineering vs. Excel/Admin) but fits the 'Process/Analyst' archetype through past Supply Chain roles. Major gaps in specific requested SaaS tools (Monday.com, Asana) and the specific Marketing domain, though their technical background indicates they could master these tools immediately. The role appears to be manual/administrative, whereas the candidate is an automation architect.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong Systems/Solutions Architect with solid Backend fundamentals (SQL, APIs, Python, Automation). The primary gap is the specific tech stack (Kotlin/Spring Boot) which is marked as mandatory/top priority. However, their experience with complex self-hosted systems, API integrations, and rapid learning of new tech (AI/LLMs) indicates high transferability of backend concepts to the JVM ecosystem.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has a strong overlap in the foundational domains: Financial Logic (Algorithmic Trading/Risk), Automation (Shell/Python), and Database (SQL). However, the role specifically demands 'Hands-on experience' with Murex MX.3 (a complex, proprietary platform) and Java, both of which are absent from the resume. While the candidate is a proven 'Fast Learner' in fintech, the requirement for existing Murex expertise for this specific 'Highly Skilled' role creates a significant gap that general adaptability cannot immediately bridge.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical domain mismatch. The candidate is a Software/Solutions Architect (IT/Tech), whereas this position requires a Civil Architect (Construction/Urban Planning). While both use the title 'Architect', the industries and hard skills (Construction Law vs. Cloud Infrastructure) are completely unrelated. No amount of 'fast learning' bridges the gap of a regulated Civil Architecture degree and legal licensing authority.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has excellent 'Product/Tech Bridge' potential and strong business acumen (Finance/Supply Chain), which aligns with the strategic aspect of the role. However, this is a 'Tech Manager' position requiring significant People Management and Agile process experience (Squads, KPIs like burnup/leadtime) in a large-scale environment. The candidate's background is primarily as a Solo Founder/Freelancer and Individual Contributor, lacking the explicit corporate engineering leadership and mobile/high-scale architecture experience required for a large fintech like PicPay.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on the critical 'Financial Services' requirement and the ability to bridge technical and business domains. The candidate excels in practical execution ('Concept to execution') and automation, fitting the 'Fast Learner' profile. However, a gap exists between the candidate's modern, agile, engineering-focused approach and the Job Description's heavy emphasis on traditional, formal project management frameworks (PMBOK, CMM, ISO) and legacy tooling (MS Project).

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate's technical depth in Systems Architecture, Automation (Python/Shell), and Infrastructure (Docker/Terraform) far exceeds the technical requirements for a platform installation and maintenance role. Their background in building complex self-hosted systems ensures they can easily handle upgrades, patching, and configuration tasks. The only true gap is specific familiarity with the proprietary Olos application.

[ANALYSIS_REPORT] [Model: gemini-cli] High match. Candidate is a senior-level Python engineer with strong Infrastructure/DevOps overlap (Docker, CI/CD, AWS) required by the role. While the resume focuses on Automation/Data/Algo Trading rather than traditional Web Development (Django/Flask), his 'Fast Learner' profile (building self-hosted LLM clusters and JIT systems) indicates he can master web frameworks rapidly. The missing SQL keyword is likely an omission given his data-heavy background.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong cultural and architectural fit for the 'Builder' and 'Autonomy' requirements. The candidate's experience in building 'metal-up' infrastructure and automated systems demonstrates the capacity to handle the physical and logical operations. However, there is a specific tech stack mismatch: the role requires deep Microsoft Azure and Corporate IT Ops (Active Directory/Windows), whereas the candidate specializes in AWS, Linux, and Dev/Data Ops. Given the 'Fast Learner' trait (self-hosted LLMs, algo trading), the transition to Azure/AD is highly feasible, but the specific hard skills are currently absent.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundation in Python, SQL, and complex system logic (Algorithmic Trading, JIT Inventory). The candidate's 'Fast Learner' trait (Self-hosted LLMs) suggests high potential to master the missing specific tools quickly. However, the resume focuses on single-node data processing (Pandas) and general automation rather than the specific distributed Big Data frameworks (Spark/Hadoop) and AWS Data ecosystem required for this role.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is exceptionally qualified for a 'Junior' role, exceeding expectations in Python, Infrastructure (Docker/Terraform), and Data/AI. While specific enterprise keywords like 'Kubernetes' (vs his generic clustering) or 'SOAP' are missing, his background as a Founder and Solutions Architect proves he can master these immediately. He fits the Python backend requirement perfectly.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a modern Cloud/DevOps Architect with strong Linux and Python skills, whereas the role is a traditional On-Premises Storage & Infrastructure Analyst. While the candidate understands capacity and performance (from Supply Chain/AI contexts) and is a proven fast learner, they lack specific experience with the required enterprise storage hardware (Dell EMC, Pure) and virtualization platforms (VMware) which are central to this position.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong technical match for the 'AI-first' and 'Integration' aspects (ERP logic, Python, APIs) and demonstrates the 'Fast Learner' trait via self-taught Algo Trading and LLM deployment. However, the role is a domain-specific Presales position requiring '3+ years of experience in Brazilian Tax' and knowledge of specific competitor solutions. While the candidate understands Finance/Supply Chain, the specific Tax Domain expertise is a critical missing hard skill that cannot be immediately bridged by technical aptitude alone.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundation in Python, automation, and data manipulation (Pandas/NumPy) with relevant context in AI infrastructure. The candidate's 'Fast Learner' trait (Founder, Quant) suggests ability to bridge gaps quickly. Primary mismatch is the specific Enterprise Data Engineering stack (Spark, Airflow, Databricks) vs. his background in custom scripting and local infrastructure.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate possesses a rare 'Unicorn' combination of strong Python/Engineering skills and deep Supply Chain expertise (MITx), directly matching the company's industry. While the profile leans more towards 'Systems Architect/Integrator' than a traditional academic 'Model Training MLE', the proven ability to self-host LLMs and build production inventory automation demonstrates the required 'Figure it out' capability. Gaps are limited to specific big-data tools and formal large-team processes.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong match for a technical Project Manager role due to extensive 'Founder' and 'Head of Engineering' experience involving full lifecycle management (Concept to Execution) and complex data analysis (Python/Pandas). His 'Fast Learner' profile and Mechatronics background satisfy the technical understanding and degree requirements. The primary penalty is the lack of formal PM methodologies (Agile/PMP certifications) and specific advertising domain experience.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate has strong implicit Project Management experience via 'Head of Operations' and Supply Chain roles (planning, risk analysis, resource management). While the resume highlights technical architecture, the 'Founder' role demonstrates the required 'concept to execution' capability. Missing specific PM tool keywords, but his technical background ensures rapid adaptation.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the architectural and automation mindset required. The candidate excels in the niche 'AI for Infrastructure' requirement (Self-hosted LLMs, GPU Clusters), which is a rare value-add. Core engineering skills (Terraform, Docker, Python, Linux) are solid. The score is held back only by the 'Flavor' mismatch: the role is heavily Azure-centric (Azure DevOps, AKS), while the candidate's cloud experience is 'AWS Basics' and local 'metal-up' infrastructure. Given the 'Fast Learner' trait and transferability of IaC/Container concepts, he can likely bridge the Azure gap quickly.

[ANALYSIS_REPORT] [Model: gemini-cli] Fundamental mismatch. The candidate is a strong Backend/Infrastructure Architect, while the role requires an Expert/Lead in Frontend (Angular). While general engineering concepts (CI/CD, APIs) and English match, the candidate lacks 100% of the core required technical stack for this specific position.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural fit. The candidate bridges the gap between 'Software Engineering' and 'Math/Stats' exactly as requested. His background in Quantitative Finance and Mechatronics covers the 'modeling and statistics concepts', while his recent work in 'Automated JIT inventory' and 'Self-hosted LLMs' proves he can build the 'ML-enabled systems' and 'pipelines' required. The 'Fast Learner' trait is highly relevant here, as he moves from self-hosted 'metal-up' infrastructure to Enterprise Cloud. The only significant gaps are the specific enterprise-grade tools used for orchestration and big data at a bank's scale.

[ANALYSIS_REPORT] [Model: gemini-cli] High potential match due to exceptional domain alignment (Supply Chain/Logistics background matches Descartes' core business) and strong foundational DevOps skills (Python, Terraform, Docker). The candidate's experience building 'metal-up' AI clusters demonstrates the required SRE mindset and 'Fast Learner' ability. The score is docked primarily for lack of explicit experience with Kubernetes, multi-cloud breadth (GCP), and specific ecosystem knowledge (Java/.NET/Windows) required for this role.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate shows strong aptitude for the core logic of the role (Data Pipelines, Python/Pandas, System Automation) demonstrated by JIT systems and Algo Trading. However, there are significant gaps in the specific Enterprise Data Engineering stack requested (Kafka, Airflow, Redshift). While his 'Fast Learner' profile suggests he could adapt (moving from Pandas/Local to Cloud/SQL), he lacks the immediate 'Mastery' of SQL and Data Warehousing tools required for Day 1 productivity.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly capable Systems/Software Architect applying for a physical Engineering Project Manager role in the Energy sector. While the Engineering degree matches, there is a fundamental domain mismatch. The role focuses on field operations, construction sites ('canteiro'), and energy regulations, whereas the candidate's strong track record is in digital automation, software, and financial supply chain.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the Data Science context given the candidate's background in Quant/Trading and Self-Hosted AI (LLMs). While specific backend stack keywords (FastAPI, Postgres, Mongo) are not explicitly listed, the candidate's experience in architecting end-to-end Python automation and 'metal-up' systems demonstrates the required technical depth. The 'Fast Learner' trait strongly suggests ability to close the specific database/framework gaps rapidly.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong foundational match in Data Engineering, Python, and System Architecture. The candidate possesses the requested 'Fast Learner' traits (self-hosted LLMs, algorithmic trading) and requisite leadership background (Head of Engineering). However, the role specifically demands 'Advanced' experience with the Azure Data Stack (Databricks, Event Hubs, ADLS) and the specific 'Medallion Architecture' pattern. While the candidate has adjacent skills (AWS basics, Data Automation, Real-time crypto systems), the lack of specific Azure enterprise tooling is a significant gap for a 'Technical Lead' expected to mentor on those specific technologies.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on domain (Identity/SSO) and Infrastructure/DevOps mindset, fitting the 'Ownership Mentality' well. However, the role strictly requires a foundation in Systems Languages (Rust, Go, C++), whereas the resume is heavily focused on Python/Scripting. While the 'Fast Learner' trait and Engineering background suggest high potential to adapt, the immediate gap in compiled language experience is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the Solutions Architect/Engineering profile with solid Infrastructure, Linux, and Data background. The candidate's 'Fast Learner' trait and experience with self-hosted AI clusters suggest they can quickly adapt to the specific Big Data ecosystem. However, the 'MUST' requirement for Spanish is a significant hard gap (despite Native Portuguese adjacency), and there is a divergence in the coding stack (Python vs. Java/.NET required).

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong Python engineer and Systems Architect with relevant automation and infrastructure experience (Terraform, APIs, Data Logic). However, this is a short-term (2-month) specialized role requiring immediate expertise in Oracle Cloud (OCI) and Apache Spark. While the candidate has high potential to transfer skills from AWS/Pandas, the lack of specific 'Proven Experience' in OCI and Distributed Processing (Spark) is a significant gap for a brief contract.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Python-based Systems/Automation engineer with impressive self-taught infrastructure skills. However, this Data Engineer role specifically mandates a Java and Big Data ecosystem (Hadoop/Spark/MapReduce) which is entirely absent from the resume. While the candidate's 'Fast Learner' trait suggests they could adapt, the gap between single-node Python automation and distributed Java-based Big Data pipelines is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong technical alignment with the 'Data & AI' domain (LLMs, Quant Trading, Python). The candidate brings deep practical knowledge of the technologies being managed. However, the resume highlights an 'Architect/Engineer' profile rather than a 'Project Manager'. Key gaps exist in formal management frameworks (Agile/PMBOK) and the specific administrative duties of a PM (managing contract profitability, hours, and large cross-functional teams) compared to his hands-on builder background.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong technical match for the foundational tools (Python, Docker, CI/CD, APIs) and demonstrates exceptional 'figure it out' capability suitable for learning the frameworks. However, the role is strictly a QA/Test Automation position, while the candidate's profile is heavy on Backend Architecture, DevOps, and Data/Algo Trading. There are significant gaps in specific QA ecosystems (Robot, Cucumber) and the Frontend/Mobile testing domains.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong Python engineer with a systems mindset (Mechatronics/Automation), but this specific role focuses on low-level networking internals (packet manipulation, TCP/UDP mechanics) and C++ analysis, which are not present in the recent professional history. While the engineering background suggests the ability to learn bit-level logic, the immediate requirement for Game Networking (Unreal) and Security protocols constitutes a significant domain gap.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong cultural and technical fit for the 'DevOps' methodology. The candidate demonstrates elite automation capability (Zero-maintenance JIT systems, Algo trading) and foundational infrastructure skills (Terraform, Docker, Linux, CI/CD). The 'Fast Learner' trait (Self-hosted LLMs) suggests rapid adaptability. The score is deducted solely because this is a Senior role requiring 'Proven Experience' specifically in Azure and Kubernetes, whereas the candidate focuses on On-Prem/Metal and basic AWS. He is a high-potential hire who would need a short ramp-up on the specific cloud provider.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly technical Solutions Architect who exceeds the core analytical requirements. His experience 'translating business goals to technical infrastructure' and 'translating financial logic' directly maps to Requirements Analysis. His expertise in 'Python Automation' and 'JIT/Supply Chain' covers the Test Automation and Process/Agile (Kanban) needs. He is overqualified but strictly lacks specific keywords for standard QA/Requirements Management tooling.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is overqualified but exceptionally well-matched for the core functional requirements. His early career in Procurement and Production Planning (5+ years) covers the requested Accounts Payable, Inventory, and Supply Chain duties. His degree in Mechatronics satisfies the 'General knowledge of engineering concepts'. His advanced technical skills (Python/SQL) vastly exceed the 'spreadsheet/database' requirements, ensuring high efficiency in project coordination.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match due to direct domain experience in Crypto/Finance and solid engineering foundations. While specific tools like DBT and Tableau are not explicitly listed, the candidate's experience with Python (Pandas/NumPy), custom ETL pipelines (JIT inventory), and financial modeling demonstrates mastery of the underlying concepts. The 'Fast Learner' profile suggests these specific tools would be trivial to acquire.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a near-perfect match, significantly exceeding the core Operations/Logistics experience requirements with a robust Engineering and Supply Chain background (MITx). His technical skillset (Python, Automation) directly addresses the 'Implement technological solutions' responsibility and technically supersedes the 'Advanced Excel' requirement, offering superior analytical capabilities. He possesses the required strategic vision and fluency in English.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptional match for the complex AI/Backend requirements, demonstrating 'deep dive' capability with self-hosted LLMs and Python automation. This aligns perfectly with the 'Agentic AI' focus. However, the role is explicitly 'Full Stack', and the resume completely lacks the required Frontend ecosystem (JavaScript, React, Node.js). While the candidate's 'Fast Learner' profile suggests they could adapt, they are currently missing half of the core technical stack.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong Systems Engineer/Architect with excellent automation and scripting skills (Python, Bash, Terraform). However, the role strictly requires 'Deep expertise' in Kubernetes (specifically EKS) and 'Hands-on' advanced AWS networking, whereas the candidate lists 'AWS Basics' and does not mention Kubernetes. While the candidate's self-hosting background suggests high aptitude for Linux/Networking, the specific container orchestration and observability toolchain required for this Senior SRE role are absent.

[ANALYSIS_REPORT] [Model: gemini-cli] Critical domain mismatch. The candidate is a Software/Systems Architect (Python, AI, Cloud, Automation), while the role is strictly for a Civil/Construction Engineer (Structural, Electrical, Hydrosanitary projects) to manage physical building permits and construction compatibility. Despite the candidate's engineering degree (Mechatronics), the hard skills required for civil construction management and municipal licensing are completely absent.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the Python and Automation aspects of Data Ops, backed by a demonstrated ability to build complex, reliable systems (JIT inventory, Trading Bots). The candidate's 'metal-up' infrastructure experience (Docker, Terraform, Self-hosted AI) suggests high capability in operations and troubleshooting. Main gaps are specific to enterprise-scale Data Warehousing tools and formal Database Administration (tuning/maintenance) in an SLA-driven environment, but the 'Fast Learner' profile indicates rapid upskilling capability here.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong candidate for a GenAI-focused backend role due to direct experience with self-hosted LLMs and AI infrastructure. Meets the ~7-year technical experience threshold (starting 2018). Proven 'Fast Learner' trait (algorithmic trading, self-hosting) mitigates specific tool gaps. Main gaps are enterprise-scale orchestration (Kubernetes) and explicit web framework experience, but core architecture skills are solid.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the Operations/Supply Chain domain (Pronutri, Electrolux experience). The candidate's advanced analytical stack (Python/Pandas) surpasses the required 'analytical profile', enabling deep process structuring and automation. While he lacks specific listed BI GUI tools, his engineering background proves superior 'systemic vision' and 'process structuring' capabilities. The role's focus on governance aligns well with his supply chain management background.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate demonstrates strong architectural fundamentals with 'metal-up' experience, Docker, and Terraform, proving deep technical aptitude suitable for a 'Fast Learner'. However, the role is explicitly focused on the Microsoft Azure ecosystem and Microsoft Stack, while the candidate's experience is centered on self-hosted/local clusters and basic AWS. While the infrastructure concepts transfer, the specific platform expertise is a significant gap for an Architect-level role.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong architectural fit for the 'Engineering' side of ML Engineering. The candidate's 'metal-up' experience and self-hosted LLM work demonstrate the required ability to connect models to infrastructure and build tooling. 'Fast Learner' trait is validated by the transition from Supply Chain to Quant to AI Ops. The primary gap is the self-assessed 'Basic' level of AWS coupled with a focus on local hardware, whereas the role requires robust cloud-based service familiarity.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a strong match for the Infrastructure, DevOps, and Python automation aspects, with a proven 'Protagonist' mindset (Founder/Self-hosted). However, for a 'FullStack Tech Lead' role, the total absence of Javascript/Typescript and Enterprise Software patterns (Microservices, Clean Architecture, SOLID) suggests a significant gap in day-to-day code leadership capabilities. GCP is missing but adjacent to known Cloud skills.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is an exceptional fit for this "Technical Project Coordinator" role. His background bridges the exact gap requested: Business/Supply Chain (MITx, Procurement experience) and deep Technical implementation (Python, Architecture, Logs analysis). While the role is titled 'Coordinator', it requires tasks like 'Log Analysis', 'Architectural Coherence', and 'Testing' (Unit/Stress), which aligns perfectly with his Solutions Architect profile. He is likely overqualified technically but fits the domain perfectly. The only gaps are specific enterprise software tools (OTM) and formal project management artifacts (S-Curve), which a 'Fast Learner' with his background can master immediately.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is significantly overqualified (Solutions Architect/Founder vs. Junior Analyst). He possesses deep domain knowledge (Trading/Finance) and advanced technical security skills (Implementing SSO/RBAC, Automation) that far exceed the manual administration requirements. While he lacks specific keywords for junior-level admin tools, his ability to build the systems implies the ability to administer them or automate the workload entirely.