[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.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate significantly exceeds the technical baseline for this Junior (IC1) role. Their background in 'Systems Engineering', 'Terraform', 'Python', and 'Local AI Clusters' aligns perfectly with the JD's focus on Automation, Infrastructure, and AIOps. The candidate's specific experience self-hosting LLMs is a high-value match for the 'AIOps' requirement. Primary gaps are platform-specific (OCI) and orchestration-specific (Kubernetes), but given the 'Fast Learner' profile and Docker proficiency, these are trivial bridges.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong domain fit: The candidate's background in Supply Chain/Logistics and 'JIT inventory systems' aligns perfectly with the 'Nice-to-have' TMS focus, often harder to find than tech skills. Architecture and 'reverse engineering' mindset (crypto bots, custom APIs) are present. Main deductions are for the specific tech stack: the role strictly requires Go (Golang) and browser automation tools (Selenium/Playwright), whereas the candidate is heavily Python-focused. Given the 'Fast Learner' profile (self-hosted LLMs), the language gap is bridgeable.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong technical match. Candidate possesses core requirement trinity: Python, SQL, and Infrastructure (Docker/Terraform/AWS). Experience building 'end-to-end logistics logic' and 'JIT inventory engines' maps directly to the required ETL/Data Pipeline tasks. 'Fast Learner' context (self-hosted LLMs, algorithmic trading) strongly mitigates the lack of specific 'Big Data' tool keywords. Main gap is the specific 'Enterprise Data Engineering' job title history versus his 'Systems/Solutions' background, and specific API protocols.

[ANALYSIS_REPORT] [Model: gemini-cli] Major role mismatch. The candidate is a high-level Solutions Architect/DevOps Engineer (Linux, Python, AI, Automation) while the job is a traditional Corporate SysAdmin/NetSec role (Microsoft, Cisco, Firewalls, Support). While the candidate is technically capable of learning, they currently lack the specific ecosystem experience (Windows Server, Active Directory, Cisco/Fortinet hardware) required for day-one operations.

[ANALYSIS_REPORT] [Model: gemini-cli] Significant role and seniority mismatch. The candidate is a Senior Systems Architect/Engineer focused on automating operations and AI infrastructure, whereas the role is an administrative assistant position focused on manual process execution (checking photos, updating reports). While the candidate is technically capable of performing these tasks, the role ignores their engineering expertise and represents a career regression from 'Head of Engineering' to 'Project Assistant'.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is a highly technical Engineering/Operations manager, whereas the role demands a Marketing/Events Project Manager. While the candidate possesses strong general project management capabilities (planning, timelines, execution), they completely lack the required domain expertise in Digital Launches, Marketing Campaigns, and Corporate Events. The mismatch between an Engineering Architect and a Marketing PM is significant.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate acts as a strong 'Technical Bridge' (Atlas Quantum) with deep experience translating business logic into technical execution (Founder/Supply Chain). Excellent match for the logic/API/integration aspects. However, the resume lacks the specific formal documentation methodology keywords (BPMN, UML, User Stories) and management tools (Jira/Confluence) required for a dedicated Senior Analyst role, though these are easily learnable for an experienced engineer.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate demonstrates exceptional process ownership and automation capability ('Dono do processo', JIT systems), aligning well with the soft skill requirements. However, there is a critical technical mismatch: the role strictly requires 'Microsoft 365/Google Workspace Migration' and corporate IT governance (ITIL, AD), while the candidate's expertise is in Linux, Python, and AI/DevOps architecture. He is likely overqualified for the Service Desk components but lacks the specific Microsoft ecosystem experience requested.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for a Python-centric Backend role. Candidate exceeds the 5+ years Python requirement (active since ~2018 in Quant/Automation roles) and meets the specific location requirement (Brazil). Core backend concepts (SQL, APIs, CI/CD, Cloud/AWS) are present. Missing skills like NoSQL and TDD are process-oriented and easily bridged given the candidate's demonstrated ability to architect complex, self-hosted AI and inventory systems.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the 'Platform' and 'Engineering' aspects of the role. The candidate excels in Python automation, Infrastructure-as-Code (Terraform), and system architecture (AWS/Docker), which meets the core requirement of building robust data platforms. The primary gaps are in specific distributed Big Data frameworks (Spark/Hive) and formal orchestration tools (Airflow), as the candidate's experience focuses on vertical scaling (Pandas/Local Clusters) rather than distributed horizontal scaling. Given his 'Fast Learner' profile and background in complex financial/supply chain data, these tool-specific gaps are likely easily bridgeable.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the 'Pleno' (Mid-Level) seniority due to robust engineering background and demonstrated ability to build complex, self-healing systems (JIT inventory, Local AI). The candidate possesses the core 'SRE Mindset' (Automation, IaC/Terraform, Scripting). The primary gaps are specific enterprise-standard tools (Kubernetes, Observability stacks) vs. his proven custom/local infrastructure experience. Given the 'Fast Learner' profile, bridging the gap from Docker/Local Clusters to Kubernetes is highly probable.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a high-potential senior engineer (Python/Infra focus) but completely lacks the mandatory core technology stack (C#/.NET) required for this specific technical leadership role. While the 'Fast Learner' trait and financial background are strong positives, the requirement to 'support the team in .NET development' makes the lack of C# experience a critical gap.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong mismatch in core domain focus. The candidate is a modern Solutions Architect/DevOps Builder (Python, Docker, Automation, AI) applying for a traditional Enterprise IT Operations/Service Management role (ITIL, Windows Server, SLAs, Support). While the candidate is 'overqualified' in engineering/logic and can easily learn the tools (Fast Learner), they lack the specific legacy administrative experience (Windows Server, Vcenter, Enterprise Networking) and the formal service management framework (ITIL) central to this role's daily duties.

[ANALYSIS_REPORT] [Model: gemini-cli] Candidate is an excellent fit, likely exceeding technical requirements. Deep experience in custom Python automation and 'metal-up' AI infrastructure serves as a strong foundation to instantly master requested low-code tools (n8n, Zapier). Financial domain experience (CryptoCall, Atlas Quantum) aligns perfectly with the 'Renda Fixa/Variável' operational context.

[ANALYSIS_REPORT] [Model: gemini-cli] The candidate is a strong analytical and technical match, exceeding expectations for 'Automation' and 'Analytical Skills' (Python/Pandas experience). His 'Systems Integrator' and 'Founder' background aligns well with the 'Enterprise Implementation' and 'Protagonist of Growth' requirements. The only distinct hard skill gap is the 'Desirable' Spanish language proficiency.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match on domain (Fintech/Algo Trading) and core backend logic (Python, Pandas, Cloud, Security). However, this is a 'Data Visualization' role and the candidate completely lacks the required Front-end stack (JavaScript, HTML, CSS) and specific Big Data orchestration tools (Kafka, Spark, Airflow), positioning them more as a Systems/Backend Architect than the requested Visualization Engineer.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match for the 'Analytics Engineer' profile, particularly the 'bridge' aspect between business logic and technical implementation. The candidate's background in building end-to-end automation (JIT systems, Trading Bots) directly translates to the required Data Pipeline and Python capabilities. While specific 'Big Data' framework experience is implicit rather than explicit, the 'Fast Learner' trait (self-hosting LLMs/Clusters) strongly suggests capability to adapt to Nubank's specific scale. The primary gaps are specific visualization tools and explicit Data Warehouse architecture terminology.

[ANALYSIS_REPORT] [Model: gemini-cli] Strong match in core domain (Finance/Trading/Algo) and technical architecture (Backend, Python, Infra). The candidate's 'Founder' and 'Self-hosted AI' background demonstrates the required 'Fast Learner' and 'System Vision' traits. Major gap is the specific 'Solid experience with people management' in a corporate engineering context (hiring, career development, 1:1s), as the resume focuses more on technical architecture and independent consulting.