[ANALYSIS_REPORT] [Model: gemini-vertex] Critical domain mismatch. The role specifically seeks an 'Expert-level Java Specialist', whereas the candidate is a Python/Systems Architect. While the candidate's DevOps and AI/LLM experience is a 'Strong Plus' per the JD, the complete absence of the primary required stack (Java, Spring Boot, React, Node.js) makes them unqualified for this specific specialist seat.

[ANALYSIS_REPORT] Strong match for the analytical and integration requirements. The candidate's background in 'Systems Integration' and 'Python/Pandas Automation' effectively covers the 'ETL' and 'API' needs, even if specific legacy tools (Pentaho/SSIS) are not listed. His experience with 'Supply Chain Analytics' and 'Financial Logic' aligns perfectly with the role's need for business logic translation. The primary gap is specific legacy SQL administration (Triggers/Procedures) and Salesforce, but his technical depth suggests these would be trivial to pick up.

[ANALYSIS_REPORT] [Model: gemini-vertex] Candidate is a strong Systems/Backend engineer with proven 'Fast Learner' traits (Quant, Self-hosting LLMs). While he lacks the specific requested language (Go) and orchestration tool (Kubernetes), his deep experience in Python, SQL, and Docker demonstrates transferrable backend fundamentals. He fits the 'Systems' aspect well but will need a ramp-up period for Go syntax and Kubernetes-specific workflows.

[ANALYSIS_REPORT] [Model: gemini-vertex] Critical mismatch in core technology. The role requires a Senior SAP Specialist to manage specific SAP support contracts (AMS) and vendors. While the candidate has strong domain knowledge in Supply Chain and Finance (matching the business side of SAP MM/FI), and general procurement experience, they lack the strictly required SAP ecosystem experience (ECC, S/4 Hana) and specific IT Service Management background.

[ANALYSIS_REPORT] Candidate meets and exceeds the 6-year Python requirement (active since 2018 in algorithmic trading and automation). Possesses required AWS exposure ('AWS Basics'). While 'PySpark' is not explicitly listed, the candidate's strong proficiency in Pandas, NumPy, and SQL, combined with a proven ability to build complex self-hosted systems (Fast Learner), suggests a very low barrier to entry for distributed data processing.

[ANALYSIS_REPORT] The candidate has strong Domain relevance (Fintech/Trading) and demonstrates Architectural capability. However, this is a Tech Lead role specifically requiring **Java** and **Golang** expertise to mentor a team, whereas the candidate is a **Python** specialist. While the candidate is a fast learner, the gap in the core technology stack and the lack of explicit corporate people management experience (PDIs, 1:1s) significantly impacts the match for a leadership position.

[ANALYSIS_REPORT] [Model: gemini-vertex] Strong cultural fit for SRE (focus on automation, self-healing systems, and 'zero maintenance'). Excellent Python and Terraform foundations. However, significant gaps exist in specific required platforms (GCP, Azure) and orchestration (Kubernetes), though the candidate's 'metal-up' experience with local AI clusters suggests high aptitude to bridge these gaps quickly.

[ANALYSIS_REPORT] Strong domain fit in Supply Chain, Finance, and ERP logic (Procurement/Inventory), plus proven API integration skills. However, this is a platform-specific role requiring 3+ years of Acumatica and C#/.NET, while the candidate is Python/Linux focused. Significant upskilling required for the specific tool and language.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate has strong foundational alignment in DevOps principles (CI/CD, Docker, Automation, Python) and a proven ability to architect complex systems (Fast Learner). However, this is a 'Tool Specialist' role focused on the Java/CloudBees ecosystem. While the candidate can likely learn the tools (Jenkins, Ansible) quickly given their Terraform/Scripting background, the requirement for practical development experience in Java/Maven or C++ represents a distinct stack gap compared to their Python/Systems focus.

[ANALYSIS_REPORT] [Model: gemini-vertex] Significant role mismatch. The position is for a Senior Project/Agile Process Analyst focused on governance, coaching, and specific frameworks (SAFe, ITIL, Scrum). The candidate is a technical Solutions Architect and Engineer focused on building, automation, and infrastructure. While the candidate has operational management experience (Supply Chain), they lack the specific formal Agile/Project Management certifications and framework expertise required.

[ANALYSIS_REPORT] Excellent fit. The candidate's profile explicitly defines them as a 'Technical Bridge', which matches the core 'Solutions Engineer' requirement of connecting business goals with engineering. The candidate possesses the required Python, API, and System Design skills with over 6 years of relevant experience. The primary gaps are domain-specific (AdTech) and specific experience with 'Big Data' at the enterprise scale, though the algorithmic trading background serves as a strong proxy.

[ANALYSIS_REPORT] Critical eligibility mismatch: The role is strictly for current students graduating 2026-2027 (Internship), whereas the candidate is a senior professional with 10+ years of experience (graduated 2014). Additionally, the role focuses on User Research/UX, while the candidate is an AI/Systems Architect.

[ANALYSIS_REPORT] Critical disqualifier: The role explicitly requires business fluency in German, which is not listed on the resume. Additionally, this is a 'Project Assistant' role focused on administrative coordination and qualitative recruitment, whereas the candidate is a senior-level Solutions Architect and Head of Engineering. The candidate is vastly overqualified and lacks the specific qualitative healthcare research focus.

[ANALYSIS_REPORT] Candidate demonstrates exceptional 'metal-up' understanding of AI infrastructure (Self-hosted LLMs, GPU clusters) and complex system automation (JIT engines), aligning well with the 'Fast Learner' and 'Builder' profiles. The score is docked primarily due to the lack of specific enterprise AI stack keywords (RAG, Vector DBs, Fine-tuning) and the disparity between 'AWS Basics' and the requirement for 'Scalable/Distributed Cloud Systems'. However, his background suggests he can bridge these gaps rapidly.

[ANALYSIS_REPORT] [Model: gemini-vertex] Major domain mismatch. The candidate is a high-level Systems Architect and Software Engineer, while the role is strictly for Automotive Regulatory Compliance (Homologation). Although the candidate possesses the required Engineering degree and English fluency, they lack the specific, non-negotiable experience with Brazilian government vehicle certification systems (INFOSERV/SISCAT) and emissions/safety testing standards required by the job.

[ANALYSIS_REPORT] [Model: gemini-vertex] Major technical stack mismatch. The candidate is a senior Python/Infrastructure/AI Architect, while the role strictly requires a PHP Web Developer with frontend skills (HTML/CSS/JS) and Windows Server experience. While the candidate is a 'Fast Learner' with strong backend logic and API experience, the core daily tools (PHP, Legacy Web) are completely absent from the resume.

[ANALYSIS_REPORT] Significant domain mismatch. The candidate is a high-level Solutions Architect and Automation Engineer, while this role is a non-technical Commercial/Account Management position specifically requiring airline industry experience. While the candidate's analytical skills (Python/Pandas) vastly exceed the requirement (Intermediate Excel), the core mandate for 'Commercial experience in the airline segment' and Spanish language proficiency are critical missing hard skills.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a strong technical fit for the 'Backend' and 'Data' verticals of this talent pool, reinforced by relevant domain experience in Supply Chain/Logistics (Syngenta context). The candidate meets the innovation and self-learning requirements. However, this is an Affirmative Action (Diversity) listing; as a Male candidate, eligibility depends on unstated diversity criteria (e.g., ethnicity, PwD). He lacks the Mobile/Frontend skills mentioned as part of the wider pool scope.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a high-potential Systems Architect with strong data engineering fundamentals (Python, ETL logic, CI/CD) and a proven 'fast learner' ability (Local LLMs, Algo Trading). However, the role is strictly a Snowflake & Azure specialist position requiring 'proven practical experience'. While the candidate can undoubtedly learn the stack rapidly, they lack the specific hard skills (Snowflake, ADF) required for immediate impact in this senior-level role.

[ANALYSIS_REPORT] The candidate has strong foundational coding (Python, SQL) and system automation skills (JIT engines, API integration), demonstrating the 'Fast Learner' trait. However, this is a Senior Data Engineering role heavily dependent on the Azure ecosystem (Data Factory, Databricks, Synapse) and specific Big Data orchestration tools (AWS Glue, Airflow) which are completely absent from the resume. While the candidate understands data logic, the specific enterprise stack gap is significant for a Senior-level position.

[ANALYSIS_REPORT] The candidate is a high-level Systems Architect with exceptional automation and programming skills (Python, CI/CD, APIs), which are a 'Big Plus' for this role. However, there is a significant domain gap: he lacks the required 3-4 years of formal 'QA Engineer' experience, the ISTQB certification, and specific mention of standard testing frameworks (e.g., Selenium, Cypress) or UI testing. While his 'Fast Learner' trait suggests he could adapt quickly, he is currently a 'Builder' rather than a 'Tester' on paper.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a capable Python-based Systems/Automation Engineer, but the role is strictly for a .NET Backend Developer. While the domain (Backend) matches, the core technology stack (.NET ecosystem) is completely missing. The candidate's experience focuses on self-hosted automation and scripting rather than the specific enterprise patterns requested (Microservices, Hexagonal Architecture). Despite 'Fast Learner' traits, the gap between Python automation and Enterprise .NET Microservices is significant for a senior/mid-level role.

[ANALYSIS_REPORT] [Model: gemini-vertex] Strong foundational match in Python, data automation, and general infrastructure (Terraform, CI/CD, SQL). However, the role is a specific 'Azure Data Engineer' position requiring the Azure data stack (Data Factory, Databricks, Synapse) and distributed processing (Spark), whereas the candidate's experience is focused on Python-based local/AWS automation and single-node data processing (Pandas). While a 'Fast Learner' capable of bridging the gap, the specific cloud ecosystem experience is currently missing.

[ANALYSIS_REPORT] Strong match due to direct Supply Chain domain expertise overlapping with the 'Supply Analytics' team focus. Candidate exceeds technical requirements for Python, SQL, and Automation ('Hacker's mindset'). The background in algorithmic trading and financial analysis covers the statistical modeling requirements. Primary gap is specific BI visualization tools (Tableau) and formal A/B testing frameworks, though the candidate's engineering background suggests rapid upskilling.

[ANALYSIS_REPORT] Strong foundational match in Automation, Scripting (Python/Bash), and IaC (Terraform). The candidate's background as a 'Systems Architect' and Founder building 'self-sustaining' JIT systems aligns perfectly with the SRE goal of operational resilience and automation. The 'Fast Learner' trait (self-hosted AI clusters) suggests high aptitude for closing specific tool gaps. Main deductions are for the lack of explicit 'Mastery' in Kubernetes (JD requires 'Domínio') and the listing of only 'AWS Basics' versus the requested Multi-cloud/Senior depth. Observability tools are a keyword gap but conceptually covered by his risk/monitoring experience.

[ANALYSIS_REPORT] [Model: gemini-vertex] Significant platform mismatch. The candidate is a Linux/DevOps-focused Architect with strong automation skills (Python), whereas the role is a classic Windows System Administrator position requiring deep troubleshooting of Active Directory and Exchange. While the candidate is a 'Fast Learner' and could likely learn PowerShell, the requirement for 5+ years of specific Windows ecosystem experience for a high-volume ticket role constitutes a major gap.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a strong technical match regarding Python, Pandas, and automation logic, with a proven "Fast Learner" profile (Algo trading, self-hosted infrastructure). However, the role is specifically for a *Senior* Data Engineer deep in the Google Cloud (GCP) ecosystem. While the candidate understands infrastructure (Terraform, AWS Basics) and data manipulation, they lack specific experience with the requested GCP suite (BigQuery, DataFlow, Composer) and formal Enterprise Data Warehousing patterns (Medallion Architecture). They are overqualified in problem-solving but would require a ramp-up period for the specific GCP toolset.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate possesses relevant domain knowledge in Supply Chain and Finance (OTC context) and has strong technical skills in API integration. However, the role specifically demands a 'Senior' Functional Analyst with deep, specific expertise in Oracle EBS modules (AR, AP, OM), which is not present in the resume. The candidate's profile is heavily skewed towards Technical Architecture and Engineering (Python, AI, Infra) rather than the Functional ERP analysis required.

[ANALYSIS_REPORT] [Model: gemini-vertex] The candidate is a strong Systems Architect with excellent Infrastructure and Python automation skills, fitting the 'Fast Learner' profile. However, this is a Senior Staff Full-Stack role requiring deep experience in Client-side development (React/Vue) and Web Frameworks (MVC), which are completely absent from the resume. While the candidate can handle the Backend and Cloud/Infrastructure aspects effectively, the lack of Frontend experience and specific software engineering methodologies (TDD) creates a significant gap for a Principal-level leadership role in a web-product company.

[ANALYSIS_REPORT] [Model: gemini-vertex] Significant domain mismatch. The candidate is a technical Solutions Architect/Engineer focused on 'building' (Python, Docker, AI, JIT Systems), while the role is a functional Business Analyst position focused on 'facilitating' (Process Management, Teams Ideas platform, Workshops). While the candidate's analytical skills are transferable, they lack the specific Product Management frameworks and the requested Microsoft 365 administrative expertise.

[ANALYSIS_REPORT] [Model: gemini-vertex] Candidate is a strong match for the underlying engineering logic, possessing deep experience in Python-based data manipulation (Pandas/NumPy), automation, and system architecture. The 'Fast Learner' trait (Self-hosted LLMs, Quant Trading) strongly suggests he can rapidly adapt his local/scripted data expertise to the required distributed Big Data stack (PySpark). However, significant tooling gaps exist: he lacks specific experience with the Databricks ecosystem, the Azure cloud environment, and enterprise messaging systems (Kafka), requiring a ramp-up to translate his backend skills to large-scale ETL pipelines.

[ANALYSIS_REPORT] The candidate is a 'Code-First' Automation Architect (Python/Shell), whereas the role is strictly for a 'Low-Code' Developer (Microsoft Power Platform). While the candidate's fundamental logic and engineering skills likely exceed the role's requirements (suggesting rapid mastery), the specific proprietary ecosystem experience is completely absent.

[ANALYSIS_REPORT] The candidate demonstrates strong 'fast learner' traits and architectural competence (Systems Integrator, Founder, Python Automation), aligning well with the role's innovation culture. However, the position specifically demands proficiency in strongly typed OOP languages (C#, Java, or Kotlin) and experience with 'large-scale SaaS' environments, whereas the candidate is primarily Python-focused with a background in self-hosted/indie systems and finance. The transition from Python/Scripting to a Senior C#/Java SaaS role is a significant gap despite the adjacent backend knowledge.

[ANALYSIS_REPORT] Major technical mismatch. While the candidate has strong domain expertise in Finance, Supply Chain, and ERP concepts (Business Logic), they lack the entire required specific tech stack (Microsoft Ecosystem). The role strictly requires 5+ years of Dynamics 365, X++, and C#/.NET development, whereas the candidate is a Python/Linux/AI infrastructure specialist. The domain knowledge matches, but the hard engineering skills do not overlap.

[ANALYSIS_REPORT] Strong match for the GenAI and Python engineering aspects. The candidate's experience with self-hosted LLMs and algorithmic trading demonstrates the required 'Fast Learner' trait and deep technical capability. Major gaps are specific Big Data frameworks (Spark/BigQuery) and the specific functional focus on Data QA automation, though his systems background suggests high adaptability.

[ANALYSIS_REPORT] The candidate has a strong foundation in data logic, Python/Pandas, and on-premise infrastructure, evidenced by building complex self-hosted systems (JIT inventory, trading bots). This demonstrates the core competency of a Data Engineer ('figuring it out' and building pipelines). However, there is a significant gap in the specific toolset requested (Airflow, dbt, NiFi). While the candidate performs data engineering tasks using Python scripts, they lack experience with the industry-standard orchestration and transformation frameworks specified.

[ANALYSIS_REPORT] Candidate has strong foundational alignment with the 'Analytics Engineer' role through proven experience in Python (production/automation), SQL, and complex data logic (JIT systems, Algo trading). While specific stack keywords like 'dbt' and 'Airflow' are missing, the candidate's track record of building 'end-to-end logistics logic' and 'custom automation pipelines' demonstrates mastery of ELT concepts. Given the 'Fast Learner' trait and self-hosted infrastructure background, learning these specific tools would be trivial.

[ANALYSIS_REPORT] Strong match in core domain (Finance/Banking) and primary language (Python). The candidate's background in quantitative analysis (Pandas/NumPy) and self-hosted infrastructure indicates a high aptitude for transitioning to Data Engineering. While missing specific Big Data ecosystem tools (Spark, Hive, NiFi), the candidate's 'Fast Learner' profile and engineering degree suggest these can be rapidly acquired.

[ANALYSIS_REPORT] Strong match on foundational engineering, automation (Python/Shell), and IaC (Terraform). The candidate's 'metal-up' and self-hosting experience proves deep technical aptitude, and the 'Fast Learner' trait suggests rapid adaptation to specific cloud tools. However, the role is a 'Senior' position specifically for AWS/Azure, and the candidate explicitly lists only 'Basic AWS' knowledge. He lacks specific enterprise observability experience (DataDog/CloudWatch) and formal large-scale SRE incident management (War Rooms) required for a bank's command center, though his diverse background mitigates this risk.

[ANALYSIS_REPORT] Strong architectural and backend engineering foundation. The candidate matches the 'Senior/Lead' profile with proven ability to build complex, self-healing systems (JIT, AI Clusters) and bridge business needs. However, there is a significant stack mismatch: the role requires advanced Golang/Typescript and Kafka/Kubernetes (Cloud Native stack), while the candidate is deeply specialized in Python and bare-metal/local infrastructure. High potential to learn, but lacks the immediate 'Specialist' fluency in the required languages.

[ANALYSIS_REPORT] Candidate has a strong engineering foundation (Python, Terraform, Docker) and 'Fast Learner' traits proven by self-hosted AI/Trading systems. However, the role is heavily specialized in Observability (Datadog, OpenTelemetry) and Kubernetes orchestration, which are absent from the resume. He fits the 'Builder' profile but lacks the specific Enterprise SRE toolchain experience.

[ANALYSIS_REPORT] Strong domain fit with Crypto/Finance background and 'Fast Learner' ability (AI/LLM clusters). However, significant gaps in specific tech stack (Coinbase is heavy on Go/Ruby, candidate is Python) and organizational scale (Freelance/Founder vs. 'Staff' level distributed systems at a major public company).

[ANALYSIS_REPORT] The candidate is currently a Systems Architect/Engineer utilizing Python and AI, whereas the role is a traditional Corporate FP&A position. While he possesses strong analytical capability (Quant/Pandas) and past financial experience (2018), he lacks the specific academic background (Admin/Accounting) and recent hands-on experience in corporate accounting cycles (Budgeting, Cash Flow) requested. He is technically overqualified but domain-misaligned.

[ANALYSIS_REPORT] Strong match. The candidate's background in 'Self-hosted LLMs' and 'Privacy-focused AI deployments' demonstrates a depth of AI knowledge that exceeds the requirement for simply 'using' Copilots. His Python expertise and Engineering degree align perfectly. The 'Privacy' focus maps directly to the AI Security/Governance requirement. The primary gap is specific enterprise tool experience (M365), but his technical AI depth compensates heavily.

[ANALYSIS_REPORT] Strong architectural and technical match. Candidate satisfies all strict requirements: Docker, multi-file project architecture (Python/JIT systems), and Latin America location. The 'Fast Learner' trait (Self-hosted LLMs) is highly relevant to the AI/AfterQuery context, bridging the gap for the specific 'task-based' nature of the role. The only notable keyword gap is explicit experience with 'Testing Frameworks' (required for the 'deterministic test suites' responsibility), though this is partially implied by 'CI/CD Pipelines' and 'Production-grade' experience.

[ANALYSIS_REPORT] The candidate is a highly capable Systems Architect and Python developer, but this role requires deep, specific expertise in the Microsoft 365 ecosystem (SharePoint, Power Platform). There is effectively zero technical overlap between the candidate's open-source/AI stack (Python, Linux, Docker) and the job's enterprise requirement (SharePoint, SPFx, Power Automate).

[ANALYSIS_REPORT] The candidate is a strong technological match for the 'Autonomy' and 'Complex Systems' aspects of the role, with relevant experience in Data (Python/Pandas) and Infrastructure (Docker/Terraform/Self-hosted AI). However, there is a significant hard skill gap: the position explicitly requires experience with **Go (Golang)** and mastery of **PostgreSQL** (optimization/modeling), whereas the candidate's stack is heavily Python-centric. His proven 'Fast Learner' ability (self-hosting LLMs, algorithmic trading) suggests he could adapt, but he lacks the immediate language proficiency required.

[ANALYSIS_REPORT] Significant technology stack mismatch. The candidate is a senior Python/Infrastructure specialist (Solutions Architect) with a focus on Automation and Data, whereas the role specifically requires a Java/Angular Fullstack developer. While the candidate has strong general engineering experience and 'Fast Learner' traits, the absence of the core programming language (Java) and the specific frontend framework (Angular) constitutes a domain gap that would require substantial re-skilling.

[ANALYSIS_REPORT] High alignment in core domains (AI/LLMs and FinTech/Trading) and System Design. However, the candidate lacks the specific required tech stack (.NET/C# and Frontend frameworks) and specific Product Management tooling (Jira, Amplitude). The 'Founder' experience partially offsets the lack of formal PM title years, but the distinct stack gap remains.

[ANALYSIS_REPORT] Candidate is a high-potential 'Fast Learner' with strong fundamental Engineering skills (Python, SQL, Terraform, Automation) and proven success building complex data-driven systems (JIT inventory, Algo trading). While the candidate lacks experience with the specific Enterprise Data stack requested (Snowflake, Airflow, Kafka), their background in 'metal-up' architecture and self-hosted clusters suggests they can rapidly master these tools. The gap is purely tooling-specific, not a lack of domain capability.