[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: claude-sonnet-4.5] Candidate demonstrates strong systems architecture thinking, automation expertise, and foundational data engineering skills (Python, SQL, Docker, CI/CD). However, this role requires deep Azure ecosystem experience and Databricks/Spark production expertise which are completely absent from the resume. The candidate has 'AWS basics' but no Azure, no Databricks, no Spark, and no data governance tooling experience. While the architectural mindset and learning capacity are evident, the gap between current skills and required platform-specific expertise is substantial. **⚠️ Critical Gaps:** - Zero Databricks production experience (REQUIRED - core platform) - No Apache Spark experience (REQUIRED - core technology) - No Microsoft Azure experience (REQUIRED - entire ecosystem missing) - No data governance tooling experience (Purview, catalogs, lineage) **✅ Strengths:** - Strong systems architecture and automation engineering (10+ years designing self-sustaining production systems) - Proven ability to build end-to-end data pipelines (JIT inventory system with 95%+ reliability) - Business-to-technical translation skills (Finance/Supply Chain background aligns with consultative data architect role) **❌ Missing Required:** Databricks production experience, Spark in production environments, Azure Synapse, Azure Data Factory, Azure AD/AAD, Data governance implementation (Purview or equivalent), Star schema and data warehouse modeling, CI/CD for data pipelines (ARM/Bicep/Terraform + Azure DevOps), Data lake/warehouse performance optimization, LGPD/GDPR compliance implementation

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Pro] Candidate is an exceptionally strong match, exceeding all basic qualifications with over 8 years of relevant experience in systems architecture and development. His demonstrated ability to build complex, production-grade systems from scratch (AI clusters, JIT automation) strongly suggests he can master AWS technologies quickly. The career trajectory shows intentional growth toward this role, and he has direct experience in the preferred retail/CPG industry. **✅ Strengths:** - Deep systems architecture experience demonstrated by building production-grade AI clusters and automated JIT systems. - Exceeds basic qualifications for years of experience in relevant technical domains (8+ years vs 4+ required). - Strong alignment with preferred qualifications, including a consulting background and direct experience in the retail/consumer goods industry.

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong systems architecture, infra, DevOps and on-prem AI experience signals high fast‑learner potential and close mapping to cloud solution architecture, but there's no demonstrated, hands‑on Microsoft Azure or Microsoft licensing/CSP experience which are core to this role. **⚠️ Critical Gaps:** - Microsoft Azure platform expertise (IaaS/PaaS/Networking/Security) — core requirement - Microsoft licensing / CSP knowledge **✅ Strengths:** - Proven system architecture + production deployments (metal-up and cloud-adjacent) - DevOps and automation (Terraform, Docker, CI/CD) enabling fast Azure ramp-up - Applied ML/LLM and privacy-first infra experience useful for Azure AI/ML workloads **❌ Missing Required:** Domínio comprovado em Microsoft Azure (IaaS, PaaS, Networking, Security, DevOps), Conhecimento de modelos de licenciamento Microsoft / CSP

[ANALYSIS_REPORT] **STRONG_MATCH** [G2.5 Pro] Candidate is a strong conceptual fit, demonstrating deep experience in architecting and building automated, data-intensive systems from the ground up. While lacking direct professional experience with public clouds (Azure/GCP) and Databricks, their proven fast-learner track record and adjacent skills in self-hosting AI infrastructure indicate a high potential to quickly bridge these specific tool gaps. The career trajectory shows a clear and intentional progression into this exact domain. **✅ Strengths:** - Systems Architecture - Data-Driven Automation (JIT/ETL) - Python & API Integration **❌ Missing Required:** Databricks, Professional experience in Cloud environments (Azure, GCP, AWS)

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Candidate is a strong infrastructure architect and rapid learner with proven production systems, automation and local AI/cluster experience, but lacks demonstrated Microsoft Azure operations/architecture and Azure-specific FinOps / telemetry experience which are central to this CSA role. **⚠️ Critical Gaps:** - Azure Architecture / Operations / Governance (core requirement) - Azure Migrations & Consumption Planning (corporate cloud migration experience) - Azure FinOps / Cost Optimization (platform-specific cost tools and practices) **✅ Strengths:** - System architecture & infrastructure design for production systems - Automation and engineering using Python, Terraform and Docker - Fast learner with demonstrated ability to self-host and optimize complex AI infra **❌ Missing Required:** Experience solidly operating corporate workloads in Microsoft Azure, Knowledge of Azure Architecture, Operations and Governance (explicit), Understanding and hands-on FinOps in Azure (cost optimization tooling and processes), Ability to interpret Azure-specific telemetry and health reports

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Strong systems-architecture, IaC (Terraform), Linux and automation background with hands-on self-hosted AI and production resilience; however the role demands deep, enterprise-scale Azure expertise and 12+ years of Solution Architecture experience which are not demonstrated. **⚠️ Critical Gaps:** - Enterprise-scale Azure infrastructure expertise (design + migrations) — blocking - 12+ years of Solution Architecture experience as stated — blocking **✅ Strengths:** - Hands-on infrastructure and IaC (Terraform) with strong automation skills - Systems architect / integrator experience building resilient production systems end-to-end - Proven fast-learner working with emerging AI infrastructure and self-hosted clusters **❌ Missing Required:** Significant, demonstrable Azure infrastructure design and enterprise implementations, Proven delivery of large-scale Azure IaaS/PaaS migrations, Deep Azure networking and zero-trust architecture experience, Hybrid identity architectures (Azure AD / ADDS multi-directory integration), Mastery of Azure-native monitoring, diagnostics, and cost tools, Experience leading complex database migrations on Azure

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: gpt-5] Strong architecture and automation background, but lacks core Microsoft Dynamics 365/Power Platform/Azure CRM stack; fast-learner potential yet gaps are blocking for a Technical Architect role. **⚠️ Critical Gaps:** - Microsoft Dynamics 365 CRM ecosystem - Power Platform/Dataverse expertise - Azure DevOps and Azure services - C#/TypeScript and FetchXML for Dynamics - Dynamics 365 modules domain experience **✅ Strengths:** - Systems architecture & automation - CI/CD and Git discipline - English fluency and cross-functional leadership **❌ Missing Required:** Microsoft Dynamics 365 CRM (8+ years), Power Platform (Power Apps, Power Automate), Dataverse, Dynamics 365 SDK, Azure Services, Azure DevOps CI/CD, Logic Apps, TypeScript, C#, FetchXML, SQL Server, BPMN, UML, Experience with Sales/Customer Service/Field Service/Marketing modules, Developing plugins and custom workflows in Dynamics 365

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5] Strong solution architecture, APIs, Python/SQL, Docker/Terraform; lacks explicit Azure, microservices, and NoSQL experience. **⚠️ Critical Gaps:** - Microsoft Azure - Microservices Architecture **✅ Strengths:** - Solution architecture and systems design - Python/SQL automation and backend engineering - Cloud/DevOps with Docker/Terraform and CI/CD **❌ Missing Required:** Microsoft Azure, Microservices Architecture, NoSQL, REST API Integration Patterns, Agile Methodologies

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Strong systems-architecture, infrastructure and supply-chain experience shows rapid learning potential, but the resume lacks SAP-specific architecture and S/4HANA conversion experience which are core to this role. **⚠️ Critical Gaps:** - Proven SAP Solutions Architect track record (end-to-end SAP architecture) - Hands-on S/4HANA conversion or implementation experience **✅ Strengths:** - Broad systems-architecture and metal-up infrastructure experience - Deep supply-chain / JIT domain knowledge relevant to ERP processes - Automation, DevOps and integration experience (Docker, Terraform, CI/CD, APIs) **❌ Missing Required:** Experience as Arquiteto de Soluções SAP (demonstrable projects/roles), Vivência em projetos SAP S/4HANA (implantação e/ou conversão)

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5] Strong systems and AI background with Python, Docker/Terraform, and CI/CD; however, lacks deep AWS service expertise expected for a Principal Architect and breadth across Node/Go/TypeScript/UI frameworks. **⚠️ Critical Gaps:** - Deep AWS cloud services expertise (production-grade architecture on AWS) **✅ Strengths:** - Python application development - CI/CD and IaC (Terraform/Docker) - LLM and privacy-first AI infrastructure **❌ Missing Required:** Deep expertise with AWS cloud services

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] The candidate is a strong systems architect with proven self-teaching abilities and relevant skills in Python, IaC (Terraform), and CI/CD. However, there is a critical gap regarding deep, production-level experience with the core AWS data services required (Athena, Glue, EMR, Redshift), which are central to the role. The profile is more aligned with general infrastructure and AI, not large-scale AWS data architecture. **⚠️ Critical Gaps:** - Solid production experience with the AWS Data & Analytics stack (Athena, Glue, EMR, Redshift) is required, but the candidate only lists 'Noções Básicas de AWS' (Basic AWS knowledge). **✅ Strengths:** - Strong Python and automation skills demonstrated in production systems. - Hands-on experience with IaC (Terraform) and CI/CD pipelines. - Proven fast learner with a track record of self-teaching complex domains (AI Infrastructure, Algorithmic Trading). **❌ Missing Required:** Solid experience with distributed processing APIs (Pyspark, Flink), Experience designing/deploying Lambda or Kappa data architectures, Solid experience deploying data lake or lakehouse in production, Experience with big data technologies (Sqoop, Flume, Kafka, Oozie, etc.)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] O candidato é um arquiteto de sistemas e engenheiro de infraestrutura altamente capaz, com um histórico comprovado de aprendizado rápido e implementação de sistemas complexos. No entanto, há uma lacuna crítica e intransponível: a total ausência de experiência com a plataforma Mulesoft, que é o requisito fundamental da vaga. Suas habilidades em integração de sistemas, APIs e nuvem são conceituais e baseadas em Python/infraestrutura customizada, não na pilha de tecnologia específica da Mulesoft. **⚠️ Critical Gaps:** - A especialização do candidato é em infraestrutura de IA e automação com Python, enquanto a vaga exige um especialista na plataforma de integração Mulesoft. **✅ Strengths:** - Forte capacidade de arquitetura de sistemas do zero - Profundo conhecimento em automação e scripting com Python - Comprovada habilidade de autoaprendizagem e adaptação ('Fast Learner') **❌ Missing Required:** MuleSoft, Anypoint Studio, CloudHub, C4E Mulesoft, API Led, java sprintboot

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Strong systems-architecture, infra-as-code, Docker/Terraform and self-hosted AI cluster experience show fast ramp potential, but the role mandates Google Cloud Architect certification and demonstrable hands-on GCP/training experience which are not present. **⚠️ Critical Gaps:** - Google Professional Cloud Architect Certification - Practical GCP platform experience (Compute Engine, GKE, VPC, IAM) - Proven experience delivering technical training and hands-on product demonstrations **✅ Strengths:** - Proven systems architect who builds production-grade infrastructure end-to-end - Practical infra skills (Docker, Terraform, Linux) and experience with self-hosted AI clusters - Demonstrated fast learner and automation-first mindset (built complex JIT and ML infra solo) **❌ Missing Required:** Google Professional Cloud Architect Certification, Advanced knowledge of Google Cloud (GCP) platform, Proven experience delivering technical training and hands-on cloud demonstrations

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong systems-architecture and API/programming background with proven delivery of complex integrations and infra; lacks direct marketing-platform and mobile SDK experience required for fastest onboarding into Braze-specific implementations, but shows high fast-learning potential and relevant transferable skills. **✅ Strengths:** - Proven API & systems integration experience (end-to-end implementations) - Strong data architecture and analytics background (JIT systems, supply-chain/quant work) - DevOps and infrastructure skills (Docker, Terraform, self-hosted clusters) enabling rapid troubleshooting **❌ Missing Required:** Mobile SDK integrations (iOS/Android), Braze or equivalent marketing engagement platform experience

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Pro] The candidate is a strong conceptual fit for a Solutions Architect role, demonstrating proven system design, automation, and leadership skills. Their career trajectory aligns with the position's seniority. However, a critical technology gap exists with .NET, as the candidate's entire background is in the Python ecosystem, which lowers the score significantly despite the architectural strengths. **⚠️ Critical Gaps:** - Development and Architecture in .NET **✅ Strengths:** - Proven Systems Architecture Experience - Leadership and Team Coordination - Strong grasp of Automation and CI/CD concepts **❌ Missing Required:** Proficiency in AWS or GCP (has basics), .NET, Swagger

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] O candidato demonstra excepcional capacidade em arquitetura de sistemas, automação e infraestrutura de IA, alinhando-se com os aspectos de "IA e Automação" da vaga. No entanto, existe uma lacuna crítica na tecnologia central exigida: o ecossistema de dados SAP. A ausência total de experiência com SAP BW/HANA, DataSphere e SAC o torna um candidato fraco para uma função tão centrada em SAP, apesar de suas outras forças. **⚠️ Critical Gaps:** - Falta de experiência no ecossistema de dados SAP, que é a base tecnológica principal da vaga. **✅ Strengths:** - Arquitetura de sistemas complexos de ponta a ponta - Profunda experiência em infraestrutura de IA (clusters LLM auto-hospedados) - Automação comprovada de processos de negócios (sistema JIT) **❌ Missing Required:** SAP BW on HANA, SAP DataSphere, SAP Analytics Cloud (SAC), SAP BusinessObjects, SAP Data Governance, Azure ADF, SSIS

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5] Strong systems builder with Docker/Terraform, OIDC and SQL, but lacks core platform-engineering staples (Kubernetes, Kafka, observability) and Oracle integration stack required for this architect role. Fast-learner potential is high, so gaps are addressable with focused upskilling. **⚠️ Critical Gaps:** - Kubernetes - Kafka/RabbitMQ (event-driven messaging) - Observability with SLIs/SLOs (Prometheus/Grafana/OpenTelemetry) - Oracle Integration stack (OIC/OSB/ODI/CDC/OGG) **✅ Strengths:** - Docker e Terraform (IaC/containers) - OIDC/SAML e RBAC (segurança/identidade) - SQL/PostgreSQL e automação em Python **❌ Missing Required:** Kubernetes, Kafka, RabbitMQ, Prometheus, Grafana, OpenTelemetry, SLIs/SLOs, Helm, GraphQL, MongoDB, Redis, SonarQube, SAST, DAST, Azure, GCP, Oracle Integration Cloud (OIC), OSB, ODI, CDC/Oracle GoldenGate, LGPD

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] O candidato é um Arquiteto de Sistemas tecnicamente forte, com experiência comprovada na construção de sistemas complexos e automatizados. No entanto, existe uma lacuna crítica no conhecimento de domínio exigido (seguros) e falta experiência específica com sistemas de Gestão Documental (DMS) e Enterprise Insurance Systems (EIS), que são centrais para a função. **⚠️ Critical Gaps:** - Experiência no setor de seguros - Experiência em projetos de integração com EIS (Enterprise Insurance System) **✅ Strengths:** - Arquitetura de sistemas complexos e autônomos - Forte habilidade em automação e integração (Python/APIs) - Perfil estratégico e autônomo, comprovado como fundador e freelancer **❌ Missing Required:** Vivência com gestão documental (Document Management) e plataformas associadas

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate is a strong technical match for the Solutions Architect role, with deep experience in systems architecture, API integration, automation, and stakeholder management. While they lack direct experience with Meta's specific advertising/messaging APIs and AdTech/Marketing, their background in building complex, scalable systems and rapid learning ability makes them well-suited for the role. Minor gaps exist in domain-specific tools, but no critical blockers for technical consulting or integration work. **⚠️ Critical Gaps:** - Direct experience with Meta's advertising and messaging products **✅ Strengths:** - Complex systems architecture - API and automation expertise - Stakeholder and project management **❌ Missing Required:** Meta platform product expertise, AdTech/Advertising domain knowledge

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Candidate shows strong systems architecture, infra, Python/SQL and production ETL experience and is a fast learner with hands-on data pipeline work, but lacks several platform/tool-specific skills central to this role (Snowflake, Power BI, Coalesce, Airbyte, SQL Server/SSIS). Given the candidate's infra and data fundamentals, they could ramp to the stack in weeks with focused effort. **⚠️ Critical Gaps:** - Snowflake - Power BI - SQL Server/SSIS - Coalesce.io - Airbyte **✅ Strengths:** - Infrastructure & systems architecture (metal-up, self-hosted AI clusters, Terraform/Docker) - Practical data engineering and automation (built production JIT inventory, ETL integrations) - Strong Python + SQL experience and history of shipping production systems **❌ Missing Required:** Snowflake, Power BI, Coalesce.io, Airbyte, SQL Server/SSIS, Azure Data Factory (exposure), Medallion architecture (deep understanding), Dimensional data modeling, Jira (familiarity), Minimum 4-hour overlap with US Eastern Time (unspecified)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] Candidate is a senior architect with deep Python, automation, and system integration experience, matching core requirements for solution design, technical leadership, and intelligent automation. However, there is no direct experience with RPA tools (Automation Anywhere, Power Automate) or specific RPA frameworks, which are explicitly requested. Fast learning and strong automation background suggest high adaptability, but lack of hands-on RPA is a notable gap. **⚠️ Critical Gaps:** - Direct experience with RPA platforms (Automation Anywhere, Power Automate) **✅ Strengths:** - Python expertise - System architecture and integration - Automation of business processes **❌ Missing Required:** Hands-on RPA tool experience, RPA development (2+ years)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong infrastructure and LLM/self-hosting background plus architecture and automation experience indicate high learning velocity for Agentic AI and cloud gaps; missing some specific cloud-native and agent-platform skills but overall fit for a senior architect who can ramp quickly. **⚠️ Critical Gaps:** - AgentCore Gateway (product-specific technology not present) - Agentic AI / multi-agent systems (role central requirement) - Strong AWS cloud-native production expertise (candidate lists only basic AWS) **✅ Strengths:** - Hands-on LLM/self-hosted AI infra and GPU cluster experience - Proven systems architecture and automation (building production pipelines from concept to execution) - Security and identity fundamentals (SSO/OIDC/SAML, RBAC) combined with operational leadership **❌ Missing Required:** Agentic AI and systems multiagents, AgentCore Gateway, MCPs / formal model control plane experience, Deep AWS cloud-native architecture experience

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] The candidate is a strong backend and infrastructure architect with proven experience in Python, automation, and AI. However, there is a critical gap in required frontend technologies (React, Angular, etc.) and their stated AWS experience is basic for an architect role at a premier AWS partner. **⚠️ Critical Gaps:** - Frontend Development (e.g., React, Vue, Angular, HTML, CSS) **✅ Strengths:** - Backend & Systems Architecture (Python, SQL) - AI/LLM Infrastructure (Self-hosted clusters) - DevOps & Automation (Terraform, Docker, CI/CD) **❌ Missing Required:** Deep expertise in AWS services and resource configuration, Expertise in web technologies and frontend frameworks (React, Vue, Angular)

[ANALYSIS_REPORT] **DISQUALIFIED** [G2.5 Pro] Candidate is DISQUALIFIED due to a hard location mismatch (role is hybrid in Brasília). Despite an impressive trajectory and proven ability to master complex AI systems from scratch, there is a critical gap in the required core technology stack (Google Cloud Platform). The candidate's experience is in self-hosted/on-premise AI and AWS basics, not the specified GCP ecosystem (BigQuery, Vertex AI). **⚠️ Critical Gaps:** - Direct experience with the Google Cloud Platform (GCP) ecosystem is required and entirely absent. **✅ Strengths:** - Proven rapid learner with deep, hands-on experience building and deploying complex AI (LLM) and automation systems from the ground up. - Strong systems architecture background, bridging business needs (finance, supply chain) with technical infrastructure. - Intentional career trajectory moving progressively towards advanced technical and AI-focused roles. **❌ Missing Required:** Google Cloud Platform (GCP), BigQuery, Vertex AI, Dataflow

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5] Strong architecture and Python experience align well, but missing Azure and front-end (React/Next.js) expertise are notable gaps; fast-learner profile suggests quick upskilling potential. **⚠️ Critical Gaps:** - Azure - React - Next.js **✅ Strengths:** - Python proficiency - Systems architecture and design - Docker/Terraform with CI/CD **❌ Missing Required:** Azure, React, Next.js

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Pro] Candidate is a strong architect and AI infrastructure expert with proven automation skills in Python and a remarkable ability to self-teach complex technologies. However, there is a critical gap in direct experience with the required cloud platforms (Azure, Google Workspace, Intune), which are central to this 'Modern Work' role. **⚠️ Critical Gaps:** - Lack of hands-on experience with core required cloud platforms: Azure, Google Workspace, and Intune. **✅ Strengths:** - Deep AI Infrastructure Experience (Self-hosted LLMs) - Proven Systems Architecture & Automation (Python) - Exceptional 'Fast Learner' and problem-solver **❌ Missing Required:** Azure, Google Workspace, Intune, Entra ID

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong systems architecture, supply-chain domain knowledge (JIT, procurement) and data/analytics background (Python, Pandas, SQL, optimization/algorithms) indicate high ability to translate business logic to technical design and learn product specifics quickly; lacking hands-on experience with commercial supply-planning platforms and full lifecycle implementations reduces an otherwise very good fit. **⚠️ Critical Gaps:** - No demonstrated hands-on experience implementing one or more commercial supply-planning products (this is explicitly required and is blocking for immediate plug-and-play delivery on o9 engagements) **✅ Strengths:** - Deep systems architecture and automation experience (metal-up infrastructure, self-hosted AI clusters) - Strong data/analytics and modeling skills (Python, Pandas, algorithmic/quantitative background) - Direct supply-chain domain expertise (JIT systems, procurement, ERP-related work) **❌ Missing Required:** Direct experience implementing supply planning products (o9 or equivalent) in production customer environments, Documented full lifecycle implementation of supply chain planning solutions (including SIT/UAT and go-live) on an enterprise planning product

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Pro] The candidate is a strong systems architect with highly relevant, modern AI infrastructure experience (self-hosted LLMs) that directly maps to the job's focus on generative AI and chatbots. The profile demonstrates a clear trajectory towards this type of role. However, the complete lack of JAVA, a primary required language, presents a critical skill gap that prevents a higher score. **⚠️ Critical Gaps:** - JAVA **✅ Strengths:** - Systems Architecture ('metal-up' design and implementation) - Deep AI Infrastructure experience (self-hosted LLMs) - Proven automation and production system design (Python, SQL, JIT Systems) **❌ Missing Required:** Watson, Google Cloud

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Pro] O candidato é um forte Arquiteto de Soluções com experiência comprovada em projetar e construir sistemas técnicos complexos, o que se alinha perfeitamente com o requisito principal. No entanto, existem lacunas significativas nas habilidades comerciais, como Estratégia de Preços e experiência recente em Negociações, tornando-o um forte ajuste técnico, mas mais fraco no lado comercial para uma função voltada para o cliente. **✅ Strengths:** - Arquitetura de Soluções de ponta a ponta (demonstrada com sistema JIT e clusters de IA) - Profunda experiência técnica em Python, Infraestrutura e Automação - Capacidade comprovada de aprendizado rápido e aplicação de tecnologias emergentes (Fast Learner) **❌ Missing Required:** Estratégia de Preços e Comercial, Negociações (experiência recente)

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-4.1] The candidate is a strong systems architect with deep experience in automation, AI, and cloud basics, but lacks direct .NET/C# and Azure AI Services expertise, which are critical for this role. Their Python, Docker, and AI infrastructure skills are highly relevant, and their leadership and architecture background are strong matches. However, the absence of hands-on .NET, C#, and Azure AI Vision experience is a significant gap for this position. **⚠️ Critical Gaps:** - C#/.NET hands-on development - Azure AI Vision/Document Intelligence - Enterprise-grade Azure cloud-native architecture **✅ Strengths:** - AI infrastructure and automation - Systems architecture and integration - Leadership and technical mentorship **❌ Missing Required:** Strong proficiency in .NET and C#, Hands-on experience with Azure AI Vision and OpenAI Service, Experience with Azure platform services (App Services, Service Bus, Cosmos DB, SQL), Angular/SSRS integration experience

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] The candidate is a talented systems architect with deep experience in building automated, production-grade systems from scratch. However, his expertise lies in a custom/open-source stack (Python, Docker, self-hosted AI), which is critically misaligned with the job's focus on the Microsoft 365, Power Platform, and Azure ecosystem. While he is a proven fast learner, the gap in specific tooling and platform experience is too significant. **⚠️ Critical Gaps:** - Experience is in custom/open-source stacks (Python, Docker, local LLMs) versus the required Microsoft ecosystem (M365, Power Platform). - Lacks any demonstrated experience in Microsoft-specific administration, tenant migrations, or the M365 security suite. **✅ Strengths:** - Proven ability to architect and build complex, automated systems from the ground up. - Strong problem-solving and rapid self-teaching capabilities in complex domains (AI, Quant Finance). - Understands core infrastructure, automation, and security concepts that are transferable. **❌ Missing Required:** Power Platform (Power BI, Power Automate, Power Apps), Microsoft 365 administration (Exchange, SharePoint, Teams), Office 365 migration experience (from G Suite, file servers, etc.), Microsoft tenant unification strategy

[ANALYSIS_REPORT] **STRONG_MATCH** [Copilot: gpt-4.1] The candidate demonstrates strong expertise in solution architecture, infrastructure, automation, and integration, with hands-on experience in cloud basics, containers, security, and DevOps practices. While advanced cloud (AWS/Azure/GCP) and commercial pre-sales experience are not explicitly detailed, the candidate's rapid learning, system-building track record, and technical breadth suggest high adaptability. Minor gaps exist in specific cloud depth, FinOps/governance, and commercial proposal experience, but overall match is strong for a senior architect role. **✅ Strengths:** - Arquitetura de soluções e integração - Automação e infraestrutura - Experiência prática em segurança e DevOps **❌ Missing Required:** Cloud (AWS/Azure/GCP) avançado, Ferramentas de modelagem de arquitetura

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: claude-sonnet-4.5] Strong architectural foundation with proven system design and Python/AI experience, but lacks specific NLP/LLM production expertise and cloud-native microservices depth. Candidate demonstrates fast learner profile with self-hosted LLM experiments and full-stack automation systems. Missing formal MLOps pipeline experience and enterprise-scale API/microservices architecture, though foundational skills are present. **✅ Strengths:** - System Architecture & End-to-End Design: Proven track record building production-grade automated systems from scratch (JIT inventory with >95% uptime, zero manual maintenance) - Python & Automation Expertise: Strong Python skills with Pandas/NumPy, shell scripting, and infrastructure automation (Docker, Terraform, CI/CD pipelines) - AI Infrastructure & Self-Learning: Hands-on experience self-hosting and optimizing LLMs locally, demonstrating curiosity and ability to rapidly adopt emerging AI technologies **❌ Missing Required:** Proven NLP/LLM experience in production environments, TensorFlow or PyTorch at advanced level (only PyTorch mentioned, unclear depth), MLOps practices (CI/CD for ML, model versioning, monitoring), Microservices design and API architecture leadership, Automated testing and code review leadership, B2B product/services integration experience

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] Candidate is a highly capable Systems Architect but operates in a completely different technical domain (custom Python, AI/Infra, open-source) from the job's explicit requirements (Microsoft Teams, Microsoft Viva). Despite a matching job title and language skills, there is a critical, insurmountable gap in the core technology stack. **⚠️ Critical Gaps:** - The role requires deep expertise in the Microsoft Teams/Viva ecosystem, whereas the candidate's entire experience is in open-source and custom-built infrastructure (Python, Linux, Docker, AI Clusters). **✅ Strengths:** - Proven Systems Architecture and integration skills - Fluent in Portuguese and based in Brazil - Strong track record of automation and delivering production systems **❌ Missing Required:** Design and development experience in major Viva components, Deep knowledge and experience in Teams Telephony and app development

[ANALYSIS_REPORT] **WEAK_MATCH** [Copilot: gpt-5-mini] Strong infra architect with Docker, Terraform, Linux, CI/CD and on‑prem LLM/GPU cluster experience — good transferable skills and fast‑learner potential — but missing core Azure/Azure DevOps credentials and several Azure/Kubernetes monitoring/orchestration skills required by the role. **⚠️ Critical Gaps:** - AZ-400 or AZ-305 (explicitly mandatory certification — blocking) - Azure DevOps (role expects domain ownership of Azure DevOps tooling) - AKS / Kubernetes (role requires solid Kubernetes experience) **✅ Strengths:** - Proven metal-up infrastructure architecture and production system delivery - Hands-on Docker, Terraform, Linux and CI/CD pipelines - Experience deploying and optimizing local AI/LLM GPU clusters (privacy-focused AI ops) **❌ Missing Required:** AZ-400 or AZ-305 certification, Hands-on Azure DevOps (Pipelines/Repos/Artifacts/Boards), AKS / Kubernetes, Azure Key Vault, Monitoring stack (Prometheus/Grafana/Datadog), Harbor (container registry)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] The candidate is a proven systems architect with an impressive ability to self-learn and build complex automated systems from scratch. However, there is a critical and disqualifying gap in the core required technologies: the entire Databricks ecosystem (DBX, Delta Lake, Unity Catalog) and deep AWS-native data services. While they possess strong adjacent skills in Python, SQL, and IaC, they lack the specific, hands-on expertise required to lead the design of a modern data lakehouse on the specified platform. **⚠️ Critical Gaps:** - Databricks / Lakehouse Architecture (no demonstrated experience) - Deep AWS-native data services (resume lists 'AWS Basics' vs. required 'Deep experience') **✅ Strengths:** - Proven ability to architect and build entire automated systems from zero - Strong foundation in Python, SQL, and Infrastructure-as-Code (Terraform) - Demonstrated 'Fast Learner' with a career trajectory moving towards complex technical roles **❌ Missing Required:** End-to-end setup of Databricks workspaces and Unity Catalog, Expertise in Delta Lake internals, Advanced PySpark skills, Deep experience with AWS Glue, S3, Redshift Spectrum, Lambda, and Athena, Strong grasp of modern dimensional modeling (star/snowflake schemas)

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-5-mini] Strong systems-architecture, IaC (Terraform), Python automation and self-hosted AI experience indicate fast learning and a solid cloud foundation, but the resume lacks hands-on Amazon Connect / contact-center and many specific AWS service integrations required for an immediate lead role. **⚠️ Critical Gaps:** - Amazon Connect (platform-specific knowledge) - BLOCKING - Contact center / telephony migration and dialer expertise - BLOCKING - CRM integration with contact center/dialer (operationally critical) - BLOCKING **✅ Strengths:** - Systems architecture and end-to-end automation (metal-up thinking) - IaC and infrastructure automation (Terraform) with Linux/DevOps skills - AI/LLM infrastructure and Python-based automation (fast practical learning) **❌ Missing Required:** Amazon Connect, CloudWatch, API Gateway, AWS Systems Manager, Amazon VPC, Lambda, EC2, ECS, SNS, SQS, Knowledge/experience with Amazon Connect setup and integration, Experience with Pinpoint / Lex integrations

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: claude-sonnet-4.5] Strong architectural and systems thinking with proven ability to bridge business and technology. Candidate demonstrates end-to-end solution design experience, cloud infrastructure knowledge, and data pipeline work. The e-commerce automation system shows capability in building scalable, data-driven solutions. Main gaps are in specific geospatial data experience and retail/martech domain exposure, but the candidate's supply chain background and fast-learner profile (complex systems builder, 33yo with diverse tech stack) suggest strong adaptability. The profile aligns well with the 'bridge' role between business and engineering that HYPR seeks. **✅ Strengths:** - Proven business-technology bridge: Supply chain + finance background transitioning to systems architect mirrors the Solutions Architect role perfectly - End-to-end system ownership: Built production-grade e-commerce automation from concept to deployment with zero manual intervention - Strong architectural thinking: Self-hosted AI infrastructure, automated inventory systems, and algorithmic trading show ability to design complex, integrated solutions **❌ Missing Required:** Geospatial data experience, Retail/media/martech domain experience

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: gpt-4.1] The candidate is a highly skilled systems architect and engineer, but lacks both the required degree in Architecture and any experience in Civil Construction projects. None of the core hard skills (project supervision on construction sites, architectural design, reading/conference of blueprints, or site safety/quality in civil works) are present or inferable. The candidate's strengths are in software, automation, and infrastructure, which are not transferable to the strictly required domain of this job. **⚠️ Critical Gaps:** - Formação superior em Arquitetura - Experiência comprovada em projetos de Construção Civil **✅ Strengths:** - Gestão de projetos - Automação e integração de sistemas - Capacidade analítica avançada **❌ Missing Required:** Diploma de Arquitetura, Experiência prática em obras civis

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] The candidate is a highly capable Systems Architect and AI Infrastructure engineer with proven skills in Python, automation, and building complex systems from the ground up. However, there is a fundamental mismatch with the core technology stack required for this role, which is heavily based on Java (JBoss, JPA, EJB) and Frontend (React, Javascript). These represent critical gaps that are too significant to overcome despite the candidate's strong architectural background and learning ability. **⚠️ Critical Gaps:** - Java Enterprise Stack (Java, JBoss, JPA, EJB, etc.) - Frontend Development (Javascript, React) **✅ Strengths:** - Systems Architecture & Design - Python & Automation - Infrastructure as Code (Terraform, Docker) **❌ Missing Required:** Java, JBoss/Tomcat/WildFly, JPA, EJB, Javascript, React, Node.js, C#, Apache Kafka/RabbitMQ, TDD

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: claude-sonnet-4.5] Complete domain mismatch. The job description is for a Physical Architecture position (building/construction architecture) requiring a degree in Architecture ('Ensino Superior em Arquitetura'), driver's license CNH 'B', and 6 months experience in the architectural field. The candidate has a background in Software/Systems Architecture, Mechatronics Engineering, Supply Chain, and AI Infrastructure - completely different from physical building architecture. **⚠️ Critical Gaps:** - Physical/Building Architecture (vs Software Architecture) - Architecture Degree (Arquitetura) - Construction Industry Experience **❌ Missing Required:** Ensino Superior em Arquitetura (Architecture Degree), 6 months experience in architectural function (building/construction), CNH 'B' driver's license

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] Complete domain mismatch. The candidate is a Software/Systems Architect specializing in IT, AI, and cloud infrastructure, while the job requires a Civil/Building Architect with a degree in Architecture/Urban Planning and experience with BIM. There is zero overlap in skills or experience. **⚠️ Critical Gaps:** - The candidate's profession is Software/IT Architecture, whereas the job is for Civil/Building Architecture. This is a fundamental and non-transferable domain difference. **✅ Strengths:** - Candidate possesses strong technical skills in an entirely different field (Software Engineering and AI Infrastructure). **❌ Missing Required:** Formação em Arquitetura e Urbanismo, Conhecimento de conceitos BIM e ferramentas, Experiência em projetos de médio e grande porte (em arquitetura civil), Experiência em compatibilização de projetos arquitetônicos

[ANALYSIS_REPORT] **DISQUALIFIED** [Copilot: gpt-5-mini] Candidate demonstrates strong systems architecture, integrations, automation and fast-learning ability but lacks any proven SAP SuccessFactors / SAP HCM / BTP / ABAP experience required for this role, making them unsuitable for immediate delivery on the opening. **⚠️ Critical Gaps:** - Proven experience as an Architect for SAP SuccessFactors (end-to-end) - ABAP HCM / on-prem SAP HCM expertise - SAP BTP + CPI/SCPI integration platform development (CAP/Node/Java) - Experience integrating SuccessFactors with SAP ECC/S4 **✅ Strengths:** - Broad systems architecture and infrastructure experience - Strong automation and engineering delivery using Python - Operability expertise: Docker, Terraform, CI/CD, Linux and security **❌ Missing Required:** Experiência comprovada como Arquiteto SAP SuccessFactors, Conhecimento avançado em SAP HCM e ABAP HCM, Proeficiência em desenvolvimento de extensões no SAP BTP (CAP/Node/Java), Experiência com Integration Center, OData APIs e SAP CPI/SCPI, Responsabilidade por arquitetura end-to-end SuccessFactors + ECC/S4 + BTP

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate demonstrates strong analytical, automation, and systems integration skills, with deep experience in data-driven environments and technical system design. They have hands-on experience with ERP, analytics, and reporting, and are fluent in English and Portuguese, but lack explicit experience with CRM, BI/data visualization tools, and advanced Spanish. The candidate's fast-learning profile and broad technical foundation make them a strong fit, with only a few domain-specific gaps. **⚠️ Critical Gaps:** - Advanced Spanish language **✅ Strengths:** - Systems integration and automation - Analytical and data-driven mindset - Experience with ERP and process improvement **❌ Missing Required:** CRM platforms, BI/data visualization tools, Advanced Spanish language

[ANALYSIS_REPORT] **GOOD_MATCH** [G2.5 Pro] The candidate is a strong technical architect with impressive self-taught capabilities and relevant domain experience in finance and consulting. However, the core role is Delivery Management, which is a significant pivot from their hands-on architect background, creating a functional gap. A notable weakness is the lack of deep, required experience in AWS or Azure, with only 'basics' listed. **⚠️ Critical Gaps:** - Formal Delivery Management experience in a corporate setting **✅ Strengths:** - Deep Solution Architecture and systems design experience - Strong client-facing and ownership skills from founder/consultant roles - Proven fast learner with a strategic, business-oriented mindset **❌ Missing Required:** Advanced Cloud Implementation experience (AWS or Azure)

[ANALYSIS_REPORT] **POOR_FIT** [G2.5 Pro] Candidate possesses strong project management skills but in a completely different domain (technical infrastructure, engineering, finance). There is a critical and fundamental gap in marketing, creator partnerships, and B2B ecosystem knowledge, making them a poor fit despite their proven ability to execute complex projects. **⚠️ Critical Gaps:** - The role requires a marketing project manager, and the candidate's experience is exclusively in technical/engineering and supply chain domains. - Candidate's career trajectory is moving deeper into AI/Infrastructure, which is the opposite direction of this creator/partnerships-focused marketing role. **✅ Strengths:** - Demonstrated end-to-end project ownership from concept to execution - Proven self-starter with entrepreneurial experience - Strong systems and automation mindset **❌ Missing Required:** Experience as a marketing project manager at a brand or agency, Hands-on exposure to formats like podcasts, events, webinars, and sponsorships, Familiarity with creator marketing or brand partnerships, Understanding of the B2B sales and marketing ecosystem, Experience working with creative talent

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] Candidate demonstrates strong, modern aptitude in AI infrastructure and end-to-end systems architecture, aligning with the 'Generative AI' aspect of the role. However, there is a critical, blocking gap concerning the 'Oracle Data Integrator (ODI)' requirement, a specific enterprise ETL platform where the candidate shows no direct or adjacent experience. **⚠️ Critical Gaps:** - Oracle Data Integrator (ODI) - The role is explicitly for a specialist in this platform, and the candidate's experience is in custom Python scripting and open-source tools, a completely different domain. **✅ Strengths:** - Generative AI Infrastructure (self-hosted LLMs) - End-to-End Systems Architecture (from metal to business logic) - Python-based Automation and Logistics **❌ Missing Required:** Oracle Data Integrator (ODI), Asset & Wealth Management domain experience, ODI data modeling and mapping

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate demonstrates strong Python, data analysis, and LLM infrastructure experience, with a proven record in building and evaluating AI systems. There is clear evidence of advanced analytics, cloud basics, and end-to-end system integration. The main gap is direct experience with semantic layer tools (Cube Core, dbt, LookML) and hands-on LLM fine-tuning, but the candidate's fast-learning profile and relevant adjacent skills make them a strong fit. **⚠️ Critical Gaps:** - Direct experience with Cube Core or similar semantic layer tools **✅ Strengths:** - Python and automation expertise - LLM infrastructure and privacy-first AI deployments - Complex system architecture and integration **❌ Missing Required:** Hands-on with Cube Core or equivalent semantic layer

[ANALYSIS_REPORT] **GOOD_MATCH** [Copilot: gpt-4.1] The candidate demonstrates strong experience in infrastructure, automation, and AI, with a proven ability to build and manage complex systems. There is a clear match in technical leadership, process optimization, and automation, but gaps exist in formal frameworks (ITIL v4, COBIT, ISO), specific ITSM tools, Power BI/DAX, and contract management for AMS/ADS. The candidate's fast learning and system integration skills suggest high adaptability, but some critical governance and compliance frameworks are missing. **⚠️ Critical Gaps:** - Formal experience with IT governance frameworks (ITIL v4, COBIT, ISO standards) - Hands-on with ITSM platforms and Power BI/DAX - Direct AMS/ADS contract management **✅ Strengths:** - Complex system architecture and automation - AI and data-driven process optimization - Proven fast learner and integrator of new technologies **❌ Missing Required:** ITIL v4, COBIT 2019, ISO 20000, ISO 27001, Power BI, DAX, ITSM Tools (Helix, Jira, BMC Remedy), AMS/ADS Contract Management

[ANALYSIS_REPORT] **WEAK_MATCH** [G2.5 Pro] The candidate is a strong systems architect and Python developer with proven experience building complex, automated systems from the ground up. However, this is a dedicated Test Automation role requiring formal experience as an SDET/QA Engineer and specific proficiency in PyTest, which are critical gaps in the candidate's resume. While technically capable, their career trajectory and demonstrated experience are misaligned with this testing-focused position. **⚠️ Critical Gaps:** - Role Mismatch: Candidate's experience is as a Systems Architect/Developer, not a Test Automation Engineer. The resume lacks any evidence of a formal testing background or QA mindset. **✅ Strengths:** - Expert-level Python for systems development and automation - Strong SQL and database knowledge (Postgres/MySQL) - Proven ability to build and deploy complex systems (CI/CD, JIT, AI Clusters) **❌ Missing Required:** Experience as a Test Automation Engineer, QA Automation Engineer, or SDET, Python + PyTest (specifically PyTest), Formal experience with integration, system, and E2E testing suites, Dedicated API testing experience