Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] A vaga exige lead de engenharia de dados com AWS + Airflow + Spark + Databricks/PySpark e histórico claro como Data Software Engineer. O Candidate tem boa base de automação/sistemas e SQL, mas não comprova experiência substancial com Airflow/Spark/Databricks nem 5+ anos na função-alvo. Para melhorar: construir um pipeline real em Airflow + Spark (ou Databricks) e explicitar liderança técnica de time de engenharia de dados. **Strengths:** Strong automation and systems architecture, SQL/PostgreSQL experience, Stakeholder management and governance cadence **Missing Required:** 5+ years as Data Software Engineer, Apache Airflow, Apache Spark, AWS (substantial production experience)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] A vaga pede Databricks/Spark, SQL Server, SAS e Power BI em ambiente corporativo; o CV não evidencia essas ferramentas (especialmente SAS/Power BI/SQL Server). Apesar de boa base em automação, SQL e qualidade de dados, o stack específico é o núcleo do trabalho. Para melhorar: adicionar projeto com Databricks/Spark + Power BI e experiência com SQL Server; SAS é um gap grande. **Strengths:** Data quality and governance mindset, SQL fundamentals and production systems, Strong business-technical translation **Missing Required:** SAS, Databricks, Spark (PySpark), corporate data engineering experience with this stack

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] O JD exige dbt + AWS (S3/Glue/Athena/Redshift etc) e 5+ anos em Engenharia de Dados; o Candidate tem SQL e sistemas em produção, mas não comprova dbt, Spark/Hadoop e profundidade AWS. A parte híbrida/presencial em SP é viável, porém a aderência técnica permanece fraca. Para melhorar: adicionar projeto dbt com testes/documentação + pipeline em AWS (S3+Glue/Athena) com CI. **Strengths:** Golang/SQL capability, ETL/automation mindset, Reliable production engineering approach **Missing Required:** 5+ years as Data Engineer, dbt (advanced), AWS services listed (hands-on)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Há compatibilidade em Python/SQL/APIs e mentalidade de produto de dados, mas a vaga torna AWS obrigatório e espera experiência clara com pipelines de dados (idealmente PySpark/Databricks). O Candidate só mostra “fundamentos AWS” e não evidencia PySpark/Databricks em produção. Para melhorar: acrescentar 1 pipeline em AWS (S3 + Glue/Lambda) e um job PySpark com métricas de custo/performance. **Strengths:** API integration + security awareness, SQL/PostgreSQL experience, Automation and reliability focus **Missing Required:** AWS (hands-on, production), data engineering pipelines evidence beyond small/relational systems

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] O JD lista um conjunto muito específico e sênior (AWS Glue/Lambda/Lake Formation/Iceberg + Snowflake 4–6 anos + dbt) e exige 4–6 anos como Data Engineer. O Candidate não comprova esses serviços nem tempo de função e pode ser filtrado por “CS ou similar” dependendo do parser do ATS. Para melhorar exigiria experiência direta com Glue/Snowflake/dbt e histórico claro como Data Engineer. **Strengths:** SQL foundations, Automation/systems engineering, English fluency **Critical Gaps:** Deep AWS Glue/Lake Formation + Snowflake years requirement not met **Missing Required:** 4–6 years as Data Engineer (AWS), AWS Glue, Snowflake (4–6 years), Lake Formation, dbt (1–3 years)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Mesma estrutura de Lead Data Software Engineer: exige 5+ anos na função, liderança e stack forte em AWS + Airflow + Spark + Databricks/PySpark. O Candidate tem base boa de sistemas e SQL, mas não evidencia experiência prática nesses componentes como core do trabalho. Para melhorar: projeto real com Airflow+Spark e detalhar liderança direta de engenheiros de dados. **Strengths:** Automation and infra optimization, SQL and production delivery, Stakeholder alignment and governance cadence **Missing Required:** 5+ years as Data Software Engineer, Apache Airflow, Apache Spark, AWS (substantial production experience)

[ANALYSIS_REPORT] **TOP** [Copilot: GPT5.2] O Candidate casa muito bem com SQL + modelagem analítica + integração de fontes e tem diferencial raro: experiência profunda em ERP (TOTVS) e e-commerce com automação real. O gap principal é a ferramenta: dbt e Tableau Prep são requeridos e não aparecem no CV. Para maximizar ATS: adicionar um portfólio dbt (tests/docs) e mapear explicitamente “ERP → staging → marts → BI”. **Strengths:** ERP data expertise + data quality controls, Advanced SQL + practical modeling mindset, E-commerce domain + multi-source integration **Missing Required:** dbt, Tableau Prep (or equivalent named in CV)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] A vaga é fortemente GCP (BigQuery/Composer/Dataflow/Dataproc/PubSub) e pede 3+ anos de GCP em produção, além de larga escala e governança/observabilidade. O Candidate não demonstra GCP e isso vira filtro duro no ATS. Para melhorar seria necessário um projeto real em GCP (BigQuery + Composer + Dataform) com custos/performance e monitoramento. **Strengths:** SQL/Python/Bash fundamentals, Production mindset (reliability, constraints), Data quality orientation **Critical Gaps:** 3+ years GCP in production not met **Missing Required:** 3+ years GCP production experience, BigQuery, Cloud Composer (Airflow) / Dataform, large-scale data pipelines on GCP

[ANALYSIS_REPORT] **TOP** [Copilot: GPT5.1] This role is almost a direct hit: the candidate has deep workflow automation (n8n, Zapier), LLM and agent experience (OpenAI, Claude, LangChain, RAG), Python/SQL, API integrations, dashboards/reporting, and significant fintech and financial data exposure. The profile also shows strong systems-thinking and internal tools design. Minor improvements could include explicitly naming Make.com or similar and highlighting concrete AI agent or autonomous workflow case studies. **Strengths:** Extensive workflow automation with n8n/Zapier/Make-style tools, Hands-on LLM pipelines and agent frameworks (LangChain/RAG), Python/SQL and API integrations for data and analytics, Fintech and financial data experience from prior roles

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Para um papel de Data Engineer II (2–3 anos), o Candidate tem sinais fortes de SQL/Python, automação, qualidade de dados e comunicação — e experiência real de resolver problemas complexos. O gap é big data/warehouse (Hadoop/BigQuery) não evidenciado, então o ATS pode considerar “não comprovado”. Para melhorar: adicionar 1 projeto de DW/lakehouse (ex: BigQuery ou Spark local) + modelagem dimensional + ETL documentado. **Strengths:** Python/SQL and data analysis foundation, Data quality + governance thinking, Strong communication and cross-functional delivery **Missing Required:** big data warehouse experience (Hadoop/BigQuery)

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] Boa aderência em embeddings/indexação vetorial para RAG, dados não estruturados, Docker e integração com AI Engineers; isso está muito alinhado ao trabalho recente do Candidate. O gap é stack típica de DE em AI (Airflow/dbt/Prefect, Spark/Kafka e cloud forte) não comprovada no CV. Para subir no ATS: adicionar um pipeline “embeddings + vector DB (FAISS/Weaviate) + orchestration (Prefect/Airflow)” e explicitar experiência com streaming ou batch em escala. **Strengths:** RAG/embeddings and vector search concepts, Production systems + automation, Data quality and security awareness **Missing Required:** orchestration tool (Airflow/dbt/Prefect) in production, distributed processing (Spark) or streaming (Kafka/PubSub)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] O Candidate atende bem a fundamentos (Python/SQL, APIs, CI/CD, qualidade de dados como prática) e tem histórico forte de dados operacionais e automação. Falta evidência de PySpark/Scala e de experiência típica de Senior DE em pipelines de dados (orquestração, DW/lake, observabilidade) em escala. Para melhorar: adicionar projeto PySpark + modelo dimensional + monitoramento/alertas e explicitar “data quality checks” como testes. **Strengths:** APIs + integration experience, SQL and data integrity controls, Cross-functional ownership and debugging mindset **Missing Required:** PySpark or Scala experience (as listed), senior-level data pipeline experience in cloud context

[ANALYSIS_REPORT] **TOP** [2.5-Pro] The score is high because the candidate's recent project is a near-perfect demonstration of the required skills, including building GenAI pipelines, prompt testing, and data manipulation with Python and SQL. He has experience with all core concepts, even if not using the exact frameworks mentioned, like LangChain. To improve, the candidate could explicitly mention 'Data Pipelines' and 'ETL concepts' in his project descriptions to better match the keywords for this 'Engenheiro de dados' role. **Strengths:** GenAI Project Experience, Python and Data Manipulation, Data Pipeline Concepts

[ANALYSIS_REPORT] **LOW** [2.5-Pro] The score is very low due to a critical gap in the required cloud platform; the role demands deep experience in GCP (BigQuery, Vertex AI), while the candidate's experience is with AWS fundamentals. The job also requires specific experience with ML models in production (XGBoost) and orchestration tools (Airflow), which are not present in the candidate's resume. The candidate's general systems and AI skills are not enough to overcome the lack of the core required toolset. **Strengths:** Python, SQL, RAG Concepts **Critical Gaps:** GCP, BigQuery, Vertex AI **Missing Required:** GCP Experience, Airflow/Prefect, Production ML Model Experience

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.1] The candidate clearly exceeds the basic requirements for Data Engineer I, with strong SQL/PostgreSQL, Python scripting, ETL/ELT-style pipelines, automation, and AI-ready data workflows from the job intelligence platform. Missing pieces are primarily classic big data tools (Hadoop/Hive/Spark/EMR) and commercial ETL tools. The candidate can further align by explicitly framing past work as data engineering, emphasizing metric warehouses, and adding exposure to at least one big-data or ETL tool. **Strengths:** Experience building ETL/ELT pipelines and metric-style data stores, Strong SQL and data modeling for operational analytics, Python scripting and automation mindset suitable for AI-ready warehouses, Brazil and Mexico retail-relevant experience from supply chain and e-commerce work

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Candidate demonstrates strong LLM pipeline experience (Claude/Gemini/OpenAI APIs, prompt engineering with PID-control tuning) and has SQL/PostgreSQL proficiency. However, critical gaps exist: no professional software development title (0 years as 'Software Engineer'), no AI voice platform experience (Vapi/Retell/Twilio), no FastAPI/Flask experience (uses Go instead), and missing TensorFlow/Pandas/ML library experience. To improve: explicitly add Python API framework experience and highlight any voice/conversational system work. **Strengths:** Production LLM deployment (Claude/Gemini/OpenAI APIs), Prompt engineering with measurable variance reduction, CI/CD and full ownership mentality **Critical Gaps:** 5+ years professional software development experience (has 0 years with SE title), AI voice platforms (Vapi/Retell/Twilio), 3+ years production AI applications **Missing Required:** Professional software development experience, AI voice platform experience, Python mastery with FastAPI/Flask

[ANALYSIS_REPORT] **TOP** [2.5-Pro] The candidate is an exceptionally strong match for a Staff-level role, as his recent project demonstrates the required systems design, architectural ownership, and deep GenAI expertise. He has independently performed the duties of a Staff engineer, from architecture to deployment. The score isn't perfect only because of a potential gap in a specific cloud provider and lack of explicit microservices experience, though this can be inferred from his skills. **Strengths:** System Design & Architecture, Deep LLM & RAG Experience, End-to-end Project Ownership

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Identical JD to Job 1. Same critical gaps apply: no professional SE title, missing AI voice platform experience, no FastAPI/Flask, missing ML libraries. Candidate's Go-based production system and LLM APIs are strong but insufficient to overcome the hard requirements. Resume should emphasize Python work more prominently and any conversational/voice system exposure. **Strengths:** Production LLM deployment (Claude/Gemini/OpenAI APIs), Prompt engineering expertise, Ownership and delivery track record **Critical Gaps:** 5+ years professional software development experience, AI voice platforms (Vapi/Retell/Twilio), 3+ years production AI applications **Missing Required:** Professional software development experience, AI voice platform experience, Python mastery with FastAPI/Flask

[ANALYSIS_REPORT] **TOP** [2.5-Pro] This role is a perfect match for the candidate's recent work, which covers every single required skill: LLM APIs, Python/Node.js, advanced prompt engineering, and backend integrations. He also has experience with all the listed 'diferenciais', including RAG and vector databases. The candidate's profile reads as if it were written for this exact job description, demonstrating practical, hands-on expertise. **Strengths:** LLM API Integration, Advanced Prompt Engineering, RAG & Vector Search Concepts

[ANALYSIS_REPORT] **MEDIUM** [2.5-Pro] The candidate strongly aligns with the core AI and development aspects of the role, such as Python, SQL, GenAI APIs, and RAG. However, the score is capped due to a missing required skill in Infrastructure as Code (Terraform), which is a key component of a Cloud Engineer role. To improve, he should emphasize any automation scripts or infrastructure management he has performed, even if not using Terraform, and state his ability to learn it quickly. **Strengths:** GenAI API Integration, Python & SQL, RAG Implementation **Missing Required:** IaC (Terraform or CloudFormation)

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Third duplicate of FullStack Labs Senior AI Agent Engineer posting. All evaluation factors remain identical. Candidate's strengths in production LLM systems and prompt engineering are overshadowed by hard requirement mismatches. Would need to add Python API framework projects and voice platform experience to be competitive. **Strengths:** Production LLM deployment, Prompt engineering with variance reduction, Full ownership delivery **Critical Gaps:** 5+ years professional software development experience, AI voice platforms (Vapi/Retell/Twilio), 3+ years production AI applications **Missing Required:** Professional software development experience, AI voice platform experience, Python mastery with FastAPI/Flask

[ANALYSIS_REPORT] **TOP** [2.5-Pro] The score is very high as the role's responsibilities—building AI memory, RAG pipelines, and agentic frameworks—are an excellent match for the candidate's deep, self-directed work. He has proven experience with Python, scalable systems, RAG, and vector databases. The minor gap is the lack of a specific agent framework like LangGraph or FastAPI, but his experience building a custom Go orchestrator and webserver shows he can master these tools easily. **Strengths:** Python Production Experience, RAG Pipeline Implementation, Vector Databases & Long-Term Memory Architecture

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Fourth duplicate of FullStack Labs posting. Analysis unchanged. Candidate profile does not match the staffing agency's credential-based filtering despite having relevant technical skills in LLM systems. To improve competitiveness: build a small FastAPI project, add voice AI demo with Twilio, and reframe job titles. **Strengths:** Production LLM APIs, Prompt engineering, End-to-end ownership **Critical Gaps:** 5+ years professional software development experience, AI voice platforms (Vapi/Retell/Twilio), 3+ years production AI applications **Missing Required:** Professional software development experience, AI voice platform experience, Python mastery with FastAPI/Flask

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Fifth duplicate of FullStack Labs posting. All analysis remains consistent. Candidate's actual capabilities (production LLM pipelines, Go orchestrator, PostgreSQL) are strong but misaligned with this role's specific requirements and the agency's credential-matching process. **Strengths:** Production LLM deployment, Prompt engineering, Full-stack ownership **Critical Gaps:** 5+ years professional software development experience, AI voice platforms (Vapi/Retell/Twilio), 3+ years production AI applications **Missing Required:** Professional software development experience, AI voice platform experience, Python mastery with FastAPI/Flask

[ANALYSIS_REPORT] **LOW** [2.5-Pro] The score is extremely low because this is a corporate-style Enterprise Architect role focused on banking standards (BIAN), governance, and process-heavy methodologies (SOA, C4 modeling). This is a severe culture and experience mismatch. The candidate's background is in scrappy, hands-on building, not in defining corporate-wide standards and compliance for a bank. He has some technical skills like OAuth, but lacks the entire enterprise architecture context. **Strengths:** OAuth/OpenID Connect, Cloud Fundamentals **Critical Gaps:** Enterprise Architecture Experience, Financial Sector Regulation, Corporate Governance **Missing Required:** Experience in Corporate Architecture, Knowledge of event-driven architecture (EDA), Experience with compliance and security governance