Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] O ATS tende a ranquear bem porque o candidato demonstra SQL/PostgreSQL, pipelines de dados em produção, ingestão via APIs e forte foco em qualidade/governança (validações, controles de input) aplicado em cenários reais. O ponto fraco é não explicitar “ETL/ELT” e modelagem/datasets como entregáveis formais (ex.: camadas bronze/silver/gold, contratos de dados, SLAs) — hoje isso fica implícito nos projetos. Para subir o score, reescrever bullets em linguagem de Analytics Engineering (datasets, data quality checks, lineage, performance tuning, documentação) e quantificar latência/custos/SLAs do pipeline. **Strengths:** SQL + PostgreSQL em produção, pipelines/automação de dados + scraping com anti-bot, data quality/governança via validação e controles de input

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] This is a corporate Data Engineering role focused on Azure and Databricks. The candidate has strong SQL and data architecture skills, but lacks the specific Azure/Databricks enterprise ecosystem. The job heavily emphasizes governance and corporate standardization, which clashes with the candidate's scrappy builder profile. **Strengths:** SQL, Data Architecture, API/Generative model integration **Critical Gaps:** Azure Data ecosystem, Databricks cluster management **Missing Required:** Databricks, Azure

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] Candidate possesses exceptional theoretical and practical alignment for the SSO/OIDC/Authentication portion, having built an OAuth 2.0 system from scratch. However, the role heavily demands React, NestJS, and Prisma, which are not the candidate's core stack. The lack of a formal SE title will also act as a strong gatekeeper. **Strengths:** OIDC/OAuth 2.0 implementation, PostgreSQL, Node.js, Docker/CI/CD **Critical Gaps:** Deep frontend React expertise, NestJS modular architecture **Missing Required:** React, NestJS

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] This is a specialized Lift & Shift migration role from Azure to AWS. The candidate has foundational DevOps (Docker, Linux, CI/CD) but completely lacks Terraform and specific AWS migration tooling (MGN, DMS) required for this structured modernization program. **Strengths:** CI/CD pipelines, Docker, Linux **Critical Gaps:** Infrastructure as Code (Terraform), Enterprise Cloud Migration tools **Missing Required:** Terraform, AWS Migration tools

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] O ATS penaliza fortemente porque o core stack exigido (.NET 6+/EF Core/Angular/Azure) não aparece nem é inferível com segurança a partir dos projetos (Go/Node/SQL/LLMs). Mesmo com boa base de arquitetura, APIs e Docker, é uma migração de stack grande para um papel que pede experiência sólida e específica. Para tentar viabilizar, só com experiência comprovável em .NET/Angular (projetos, commits, apps) e Azure (App Services/Functions/Key Vault) — sem isso, o ATS tende a enterrar. **Strengths:** arquitetura de sistemas, APIs + bancos SQL, Docker/Linux/Git/CI-CD (base transferível) **Critical Gaps:** stack principal (.NET + Angular) ausente **Missing Required:** .NET 6+, Angular 8+, Azure, Entity Framework Core, SQL Server

[ANALYSIS_REPORT] **TOP** [gemini-3.1-pro-preview] The candidate is a near-perfect functional match. The role requires integrating legacy ERPs, building webhooks/APIs, implementing Gemini API/RAG, and analyzing manual workflows to create digital automations. This precisely describes the candidate's trajectory from Supply Chain ERP remediation to building LLM orchestrators. To maximize ATS, highlight 'REST APIs' and 'Digital Transformation' explicitly. **Strengths:** Gemini API & RAG integration, ERP/Legacy system data remediation (TOTVS), Workflow automation and business translation

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] This embedded systems role requires 5+ years of C/C++ in a semiconductor environment. While the candidate has a Mechatronics degree and Linux proficiency, they lack the professional C/C++ experience and work in high-level/web/Go ecosystems, not low-level embedded hardware drivers. **Strengths:** Python, Linux command line, Mechatronics background **Critical Gaps:** 5+ years professional C/C++ development, Embedded lithography domain **Missing Required:** C, C++, 5+ years development experience

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] The candidate matches the backend requirements (Python, PostgreSQL, Docker). However, the role heavily expects modern frontend frameworks (React/TypeScript), which the candidate lacks, preferring Vanilla JS. Furthermore, enterprise SaaS roles tightly gatekeep on formal Full Stack Engineer titles. **Strengths:** Python, PostgreSQL, Docker, REST APIs **Critical Gaps:** React/TypeScript frontend ecosystem **Missing Required:** React, TypeScript

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] Candidate has excellent Go and Python skills and understands Docker and IAM. However, the role is heavily skewed towards DevSecOps infrastructure (Kubernetes, Istio, ArgoCD, Helm), which the candidate has not utilized, preferring single-binary deployments and raw VPS setups. **Strengths:** Go lang, Python, Docker, IAM **Critical Gaps:** Kubernetes orchestration ecosystem **Missing Required:** Kubernetes, Helm

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] This Data Engineering role is strictly focused on streaming data pipelines using Kafka and Debezium for Change Data Capture (CDC). The candidate has PostgreSQL experience but entirely lacks the streaming ecosystem and CDC background required. **Strengths:** PostgreSQL **Critical Gaps:** Streaming architecture (Kafka), CDC implementation (Debezium) **Missing Required:** Debezium, Kafka, CDC

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] While the backend-heavy Go focus aligns with the candidate's recent projects, the role demands 5+ years of explicit Software Engineering experience, Kubernetes/Kafka, and React ability. The candidate's non-traditional trajectory and lack of React/K8s disqualify them for this high-scale product role. **Strengths:** Golang, Docker, High ownership/independence **Critical Gaps:** 5+ years formal Software Engineering experience, React frontend **Missing Required:** 5+ years SWE experience, Kubernetes, React

[ANALYSIS_REPORT] **LOW** [gemini-3.1-pro-preview] The role requires standard enterprise data engineering tools: AWS Redshift, Snowflake, Apache Airflow, and dimensional modeling. The candidate understands data architecture deeply but implements via Python/Go/PostgreSQL rather than these specific enterprise pipeline orchestration tools. **Strengths:** PostgreSQL, SQL, Data Quality, Dimensional modeling concepts **Critical Gaps:** Enterprise Data Warehouse tools, Pipeline orchestration tools (Airflow) **Missing Required:** Apache Airflow, AWS Redshift, Snowflake

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The ATS sees strong overlap in backend concepts (systems design, RESTful-style services, authentication/authorization, Docker, SQL databases, Git) and leadership, but the core stack here is C#/Angular/Vue with SQL Server/MongoDB, none of which are present on the resume. Multiple hard requirements (5+ years C#, 3+ years Vue/React plus Angular, SQL Server, MongoDB, oil & gas domain) are missing, so despite good infrastructure and automation experience the semantic match drops sharply. To improve for this JD, the candidate would need either real, clearly-documented production experience in C#/ASP.NET plus at least one SPA framework (Angular/Vue/React) and SQL Server, or a tailored version of the resume explicitly reframed around .NET and front-end contributions, which currently do not exist. **Strengths:** Backend systems design and scalable automation, Docker and containerized deployment experience, Authentication and authorization with OAuth 2.0/SSO and strong English communication **Missing Required:** 5+ years of C# experience, 3+ years of Vue.js OR React.js and Angular experience, SQL Server experience, MongoDB experience

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.2] Apesar de bons sinais em SQL, pipelines e engenharia de dados geral, o ATS para este JD costuma ser Databricks/Azure-first e exige PySpark/Delta/Unity Catalog + Airflow/ADF, que não aparecem no histórico do candidato. A falta dessas âncoras derruba o match para uma vaga Senior com governança lakehouse. Para melhorar, só com evidência objetiva (projetos/experiência) em Databricks/PySpark e orquestração (Airflow/ADF) além de Azure. **Strengths:** SQL + modelagem/validação orientada a qualidade de dados, construção de pipelines e automação end-to-end, Git/CI-CD e mentalidade de produção **Missing Required:** Databricks, PySpark, Delta Lake/Iceberg, Unity Catalog, Airflow ou Azure Data Factory, Azure (vivência)

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] O ATS gosta do alinhamento com LLM stack (LangChain/RAG/agentes), Docker/Linux, bancos/APIs e arquitetura — o candidato tem provas recentes disso em produção. Porém o JD exige 5+ anos Cloud (AWS) e 2+ anos IaC (Terraform), além de testes avançados e backend Python (Django/FastAPI), que não estão evidenciados com profundidade/tempo. Para subir, precisa explicitar experiência prática com AWS (serviços usados, deploy, IAM, observabilidade), adicionar IaC real (Terraform) e bullets sobre testes automatizados (unit/integration/e2e) em projetos. **Strengths:** LangChain/RAG/pipelines LLM, Docker/Linux + engenharia de produção, arquitetura de sistemas + APIs/DB **Missing Required:** 5+ anos Cloud (AWS), 2+ anos IaC (Terraform), testes avançados

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.1] The ATS sees a strong match on Python, systems architecture, distributed thinking, REST-style integrations, CI/CD, cloud exposure, and acting as a technical reference/bridge between business and engineering. The candidate’s recent work architecting a production job intelligence platform and a Go-based orchestrator, combined with prior ERP and automation projects, maps well to solution architecture, governance, and technical guidance requirements, even if most recent implementation details feature Go and Node rather than pure Python. To improve for this JD, the candidate should foreground Python specifically in recent projects (e.g., highlight any Python-based services or tooling, emphasize distributed designs, and add concrete examples of Python microservices and cloud deployments, plus mention any experience with event-driven patterns, observability, and containers using the JD’s exact keywords). **Strengths:** Systems architecture experience across automation, ERP, and AI-integrated platforms, Hands-on ownership of distributed orchestration, state machines, and production infrastructure, Strong CI/CD, Git, and cloud fundamentals with excellent English and stakeholder communication

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The ATS sees good alignment on Python-based pipelines, SQL, data quality, governance, and documentation, thanks to extensive work on ERP data remediation, forecasting, and recent AI/data pipelines. However, the JD’s core ecosystem (DeltaLake, Spark, Superset, medallion architecture, Parquet/Delta storage optimization) does not appear anywhere in the resume, which creates notable gaps for a modern data engineering stack. To improve for this JD, the candidate should either build and document a small Spark/DeltaLake proof-of-concept (medallion-style) and add those exact keywords, or explicitly align existing pipelines (e.g., batch/stream processing, partitioning, cost/performance tuning) with equivalent big-data concepts in the skills and project descriptions. **Strengths:** Deep experience with data quality, validation rules, and governance in ERP and operations, Python and SQL-based pipeline design with focus on forecasting and operational analytics, Ability to document architectures, data flows, and business logic clearly for stakeholders **Missing Required:** Hands-on Spark experience, Experience in DeltaLake-based ecosystems

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] O ATS tende a ranquear muito alto porque o candidato já construiu crawler de produção com anti-bot/JS dinâmico, usa Linux/Docker, integra LLMs para classificação/extração e domina APIs/DB — exatamente o coração do JD. As lacunas são OCR/visão computacional e “ML libs” (PyTorch/TensorFlow/Scikit) explicitamente, além de não citar Scrapy/Selenium/Playwright (embora Puppeteer cubra o caso). Para melhorar o match, adicionar bullets e keywords diretas: proxies/captcha handling, Playwright/Selenium (se aplicável), e qualquer experiência mínima com OCR ou pipeline de validação de documentos. **Strengths:** crawlers avançados + anti-bot (produção), LLMs/prompting para classificação/extração, Linux + Docker + APIs + bancos SQL

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.1] The ATS sees high overlap on Python backend work, scalable systems, event-driven/microservice-style thinking, Docker, CI/CD, large data handling, and startup/entrepreneurial mindset from building production systems and running an automated e-commerce business. GCP and a specific Python web framework (FastAPI/Flask/Django) are not explicitly documented, and the "7+ years of backend software development with Python" requirement is not clearly evidenced by titles or project descriptions, which pulls the score down and triggers the missing_required cap. To improve for this JD, the candidate should highlight any existing Python web services, select and emphasize one framework (e.g., FastAPI or Flask), add explicit cloud experience (even if small) in Google Cloud or mapped concepts, and tie their automation and data-processing work directly to event-driven patterns using the JD’s language. **Strengths:** Extensive experience designing scalable, event-driven-style automation systems and orchestrators, Solid Python, data processing, and analytics background applied to real operations, Strong Docker, CI/CD, and startup/entrepreneurial experience, including founding and running a business **Missing Required:** 7+ years of backend software development with Python (explicitly demonstrated), Strong expertise in FastAPI, Flask or Django, Explicit production experience on GCP

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.1] The ATS sees a very strong match: JavaScript/Node.js, SQL and relational databases, data pipelines, AI/ML, NLP-style work, data governance, and translating business data stories into technical specs are all directly reflected in past ERP, forecasting, and job intelligence projects. The role’s focus on building data products, scalable pipelines (batch/stream), governance, and supporting analytics aligns extremely well with the candidate’s history of cleaning corrupted ERP data, designing automation workflows, and building AI scoring/reporting systems; missing items are mostly specific tooling names (e.g., particular streaming platforms). To further optimize for this JD, the candidate could frame their recent platform as a "data product" with explicit mention of streaming/batch modes, add any experience with specific streaming tools (Kafka, Kinesis, Pub/Sub) if applicable, and emphasize data modeling, dimensional design, and documentation in the language the JD uses. **Strengths:** JavaScript/Node.js and SQL-based data pipeline development with production deployments, Hands-on data remediation, governance, and validation rule design in ERP and operational contexts, Experience building AI-driven scoring and analytics workflows end-to-end

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] The ATS finds strong signals for Python backend development, data infrastructure orchestration, microservices-style design, SQL/NoSQL familiarity, CI/CD, testing mindset, and scalable systems, all supported by the candidate’s job intelligence platform, e-commerce automation, and quant/ERP projects. However, explicit Django experience, years of professional Python development under a software engineer title, and some of the formal architecture/testing buzzwords (CLEAN Code, SOLID, design patterns) are not clearly evidenced on the resume, so the score is capped due to missing_required items. To improve for this JD, the candidate should surface any Django or web-framework adjacent Python work, mirror the JD language around SOLID/CLEAN/design patterns and testing, and quantify Python-heavy contributions (with years and project scale) to satisfy the 5+ years expectation more clearly. **Strengths:** Significant Python, SQL, and automation experience in production systems, Demonstrated ability to design and implement whole applications from scratch, Strong CI/CD and Git experience plus mentoring/leadership capabilities from founder/head-of-engineering role **Missing Required:** 5+ years of practical Python and Django development (clearly documented), Explicit microservices and cloud deployment using the employer’s preferred stack

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The JD demands 5+ years of experience in software engineering with C#, plus test automation and MS SQL Server, while the resume shows no C# or dedicated software engineer titles—only business/operations and founder roles that include engineering work. Modern enterprise ATS systems typically treat "5+ years of software engineering" with a specific language as a hard filter when the language (C#) and a matching title are absent, so this profile is auto-rejected before semantic strengths can help. To be competitive for similar roles, the candidate would need demonstrable C# experience (projects, repos, achievements) and at least one formal software engineering title, both called out explicitly with matching keywords and technologies. **Strengths:** Advanced English and strong communication skills, Experience with relational databases and SQL in production contexts, Proven general programming ability in Go, Python, and JavaScript **Missing Required:** 5+ years of experience in software engineering (with a software engineer title), C# programming experience, MS SQL Server experience

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.2] Boa base de infra (Linux/Docker/Git/CI) e perfil hands-on para troubleshooting, mas a vaga é Azure-first e o CV não prova experiência prática em Azure. ATS tende a classificar como “cloud genérico” e penalizar por falta do vendor específico. Inglês é positivo, porém o foco em tickets/rotina enterprise reduz o match percebido. **Strengths:** Linux + Docker (infra), Automação/observabilidade em produção (inferido), Inglês fluente **Missing Required:** Domínio de ambiente Azure (hands-on)

[ANALYSIS_REPORT] **LOW** [Copilot: GPT4.1] No Databricks, PySpark, or AWS deep expertise; no evidence of enterprise-grade data platform design. ATS will see critical gaps in required stack. Role requires deep, hands-on Databricks and AWS, which are not present. **Strengths:** System architecture, Python, SQL **Critical Gaps:** Databricks, PySpark, AWS (deep) **Missing Required:** Databricks, PySpark, AWS

[ANALYSIS_REPORT] **HIGH** [Copilot: GPT5.2] Match forte para arquitetura de soluções: trade-offs, governança técnica, cloud, APIs e CI/CD batem bem com o histórico de arquitetura e entrega end-to-end. ATS tende a gostar do foco em escalabilidade/segurança e atuação como referência técnica. Ficam lacunas em mensageria/arquitetura distribuída “clássica” e certificações cloud. **Strengths:** Arquitetura de sistemas + decisões por trade-off, Cloud + DevOps/CI-CD (declarado), Integração/API + ponte negócio-tecnologia