Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **TOP** [Opencode: opencode:glm-5] Near-identical to Job 10 but Senior level (3-5 years experience preferred vs 5-7 for Lead). All same strengths: Python, APIs, AI models, financial services background. Non-traditional backgrounds explicitly welcomed. This is slightly more accessible than the Lead role. **Strengths:** Python production code, financial services domain, AI/ML integration

[ANALYSIS_REPORT] **HIGH** [Opencode: opencode:glm-5] Strong technical match: SQL, Python, ETL pipelines, data architecture, team leadership all demonstrated. Code review and deployment approval experience shown in resume. Missing formal 'Data Engineer' title but has extensive systems engineering and data pipeline work. Brazil-based role is perfect geographic fit. **Strengths:** SQL/Python proficiency, ETL/ELT pipeline architecture, team leadership and code review

[ANALYSIS_REPORT] **LOW** [Opencode: haiku-4-5] Critical gaps: Candidate has zero SAP ecosystem experience. No SAP BTP, CAP, CDS, OData, XSUAA, Fiori, or SAP HANA Cloud knowledge. While candidate has Go/Node/JavaScript/Python, and understands APIs/cloud, SAP BTP is a completely vertical stack requiring specialized learning. No evidence of Java or Node.js production development (Go preference). JD requires 'comprovada experiência' (proven experience) with SAP BTP/CAP—this is a hard requirement. **Strengths:** Go backend development, API design, PostgreSQL/SQL, Cloud deployment mindset **Critical Gaps:** No SAP ecosystem exposure **Missing Required:** SAP BTP and CAP proven experience

[ANALYSIS_REPORT] **LOW** [Opencode: opencode:glm-5] Role is specifically for Power BI cloud implementation with SharePoint connectors, Azure AD permissions, and RLS schemas. Candidate has no demonstrated Power BI, SharePoint, or Azure AD experience. SQL and data modeling transfer but the core tooling is entirely absent. **Strengths:** SQL proficiency, data modeling concepts, Advanced English **Missing Required:** Power BI cloud implementation, SharePoint connectors, Azure AD permissions management, RLS schema design

[ANALYSIS_REPORT] **TOP** [Opencode: opencode:glm-5] Strong platform engineering match: Python, CI/CD, data platform engineering, observability, Airflow all demonstrated. Data mesh concepts align with architecture approach. Missing Snowflake specifically but has PostgreSQL production experience. This is a platform engineering role that values autonomy and delivery. **Strengths:** Python platform engineering, CI/CD automation, Airflow orchestration, observability mindset

[ANALYSIS_REPORT] **LOW** [Opencode: opencode:glm-5] Role requires 7+ years as Microsoft SQL Server DBA with specific expertise in T-SQL, SSIS, SSRS, PowerShell, and Azure SQL. Candidate's database experience is entirely PostgreSQL-based. SQL Server DBA is a completely different domain from data engineering/PostgreSQL work. **Strengths:** SQL query optimization concepts, database architecture thinking **Critical Gaps:** SQL Server DBA expertise **Missing Required:** 7+ years SQL Server DBA, T-SQL proficiency, SSIS/SSRS experience, PowerShell scripting

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This role requires deep expertise in Microsoft Fabric (Lakehouse, Data Factory, Spark), Power BI (DAX, star schema, semantic models), and marketing analytics domains — none of which the candidate possesses. The candidate has SQL and Python adjacency but zero Microsoft Fabric/Power BI experience, which are the core of this role. Additionally, Capco is a big consulting firm (explicit RED FLAG) and requires specific Microsoft certifications (DP-700, DP-600, PL-300). **Strengths:** SQL knowledge, Data pipeline concepts, English C2 **Critical Gaps:** Microsoft Fabric ecosystem (completely different domain), Power BI semantic models and DAX, Marketing analytics data domains **Missing Required:** Microsoft Fabric experience, Power BI modeling, DAX, PySpark/Spark SQL in Fabric context, Marketing analytics domain knowledge

[ANALYSIS_REPORT] **HIGH** [Opencode: opencode:glm-5] Strong data engineering match: Python, SQL, API development, Airflow, PostgreSQL, Docker all demonstrated. Missing Terraform, Kubernetes, Spark/Kafka streaming specifically, but core data engineering skills are solid. TRM's culture emphasizes ownership, speed, and impact—strong alignment with candidate's approach. **Strengths:** Python data engineering, Airflow orchestration, distributed systems thinking, production deployment

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has Python, SQL, Git, and data pipeline concepts but lacks the core Analytics Engineer stack: no experience with dbt-style modeling (conceptual/logical/physical at scale), no Power BI/Looker Studio proficiency for strategic dashboards, no Airflow or pipeline orchestration experience, and no proven data modeling at enterprise scale. The candidate's SQL is functional but not at the 'excellence in highly optimized and scalable queries' level required. Some adjacency through ERP data work and pipeline concepts provides partial credit. **Strengths:** Python programming, SQL knowledge, Git/version control, AI/ML differential matches candidate's GenAI experience **Critical Gaps:** Data modeling expertise at enterprise scale is the core of this role **Missing Required:** Data modeling in complex large-scale environments, Data visualization tools (Power BI/Looker Studio), Pipeline orchestration tools (Airflow)

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has relevant adjacency: Linux, Docker, CI/CD concepts, system architecture knowledge, PostgreSQL, and monitoring concepts from production systems. However, the role requires specific SRE/DevOps experience with cloud providers (AWS/Azure/GCP), Kubernetes orchestration, APM tools (Dynatrace/Datadog/New Relic), Infrastructure as Code, and prior SRE/DevOps/Infrastructure role experience. The candidate has never held an SRE or DevOps title, and while the orchestrator project shows systems thinking, it doesn't demonstrate cloud-native SRE practices at scale. **Strengths:** Docker experience, Linux proficiency, System architecture understanding, CI/CD concepts **Critical Gaps:** No prior SRE/DevOps/Infrastructure role experience as explicitly required **Missing Required:** Prior SRE/DevOps/Infrastructure role experience, APM tools, Kubernetes, Cloud provider proficiency, Observability and monitoring hands-on

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has strong supply chain domain knowledge and some Power BI adjacency through data work, but this role requires deep Microsoft PowerPlatform expertise (PowerApps, Power Automate, SharePoint, Microsoft Lists) which the candidate completely lacks. The supply chain domain match is a positive, and the candidate's automation/workflow experience is conceptually adjacent, but the specific tooling gap is a critical blocker. Siemens Healthineers is a large enterprise (73,000+ employees) which is a weak red flag. The JD also requires a CS/IS degree. **Strengths:** Supply chain domain expertise, Business-technical translation ability, Automation and workflow design, English C2 **Critical Gaps:** Microsoft PowerPlatform ecosystem (completely different tooling domain) **Missing Required:** PowerPlatform development experience, PowerApps, Power Automate, CS/IS degree (has Mechatronics Engineering)

[ANALYSIS_REPORT] **LOW** [Opencode: opencode:glm-5] Role requires strong ML experience with Pandas, NumPy, Scikit-learn, XGBoost, PySpark for model training. Candidate has AI integration experience but no demonstrated ML model training with these libraries. The focus is on training models, not inference pipelines. **Strengths:** Python proficiency, SQL for large datasets, English B2+ **Missing Required:** Pandas/NumPy/Scikit-learn experience, XGBoost/PySpark proficiency

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opencode:glm-5] Good data engineering alignment: Python, SQL, Docker, AWS, data pipelines demonstrated. Critical gap: No graph database experience (Neo4j, Neptune) mentioned—this is the core specialization of the role. Enterprise environment with 280M annual visitors suggests scale experience needed. **Strengths:** Python proficiency, SQL expertise, Docker containerization, data pipeline architecture **Missing Required:** graph database experience

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This is a Senior Software Engineer role requiring 5+ years in Java or Node.js, 2+ years AWS Cloud, Terraform IaC, Jenkins CI/CD, and observability tools (NewRelic, OpenTelemetry). The candidate has Node.js and some AWS fundamentals, but lacks Java entirely, has no Terraform experience, no Jenkins experience, no NewRelic/OpenTelemetry experience, and critically has 0 years with a Software Engineer title. The role requires hybrid work in Indaiatuba/SP which is feasible but adds geographic constraint. The 5+ years professional SE experience is an ATS hard filter. **Strengths:** Node.js/JavaScript experience, PostgreSQL, Go (modern language, shows programming ability), CI/CD concepts **Critical Gaps:** No Software Engineer title (0 years professional SE), No Java experience (primary language requirement) **Missing Required:** 5+ years software engineering experience, Java proficiency, 2+ years AWS Cloud, Terraform IaC, Jenkins CI/CD, NewRelic/OpenTelemetry

[ANALYSIS_REPORT] **LOW** [Opencode: opencode:glm-5] Role is specifically for Computer Vision engineering—image classification, object detection, segmentation, OpenCV, YOLO, Detectron2. Candidate has zero demonstrated computer vision experience. LLM/RAG work is completely different from CV. This is a critical domain gap. **Strengths:** Python proficiency **Critical Gaps:** Computer Vision experience **Missing Required:** 3+ years Computer Vision experience, PyTorch/TensorFlow, OpenCV/YOLO

[ANALYSIS_REPORT] **TOP** [Opencode: opencode:glm-5] Near-perfect fit: Strong Python, SQL/Postgres, data pipelines/ETL, Airflow, AWS all directly demonstrated. Energy startup values ownership, pragmatism, and curiosity—all explicitly shown in candidate's approach. Missing formal Senior Data Engineer title but 'senior-level potential' language in JD suggests growth mindset is valued. **Strengths:** Python data engineering, SQL/Postgres proficiency, Airflow orchestration, AWS fundamentals

[ANALYSIS_REPORT] **LOW** [2.5-Pro] The candidate shows strong proficiency in Python, SQL, and PostgreSQL, which are core requirements. However, the ATS score is significantly penalized due to the absence of specific, required data engineering tools like Apache Airflow, PySpark, and AWS Glue. His custom orchestrator demonstrates conceptual understanding but does not count as a direct keyword match for these enterprise tools. **Strengths:** Python, PostgreSQL, SQL **Missing Required:** Apache Airflow, PySpark, AWS Glue, 4+ years of production data engineering experience

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has relevant adjacency in several areas: LangChain experience, CI/CD concepts, Docker, Linux, and AI integration experience. The role requires Lead DevOps with solid Azure and AWS experience, Kubernetes orchestration, Terraform IaC, and technical leadership of DevOps teams — areas where the candidate has limited or no experience. However, the AI/LLM integration aspect (LangChain, LlamaIndex, AI Ops) is a strong match as a desirable skill. The role is 100% remote, which is favorable. Dentsu is a large company but the DWS Brazil unit operates more like a tech center. **Strengths:** LangChain experience (desirable), AI integration in production, Docker experience, Linux proficiency, English C2 (advanced required) **Critical Gaps:** No DevOps/Infrastructure role experience as Lead **Missing Required:** Solid Azure and AWS experience, Kubernetes orchestration, Terraform/IaC, Cloud networking and security, CI/CD pipeline mastery, DevOps team leadership experience

[ANALYSIS_REPORT] **LOW** [2.5-Pro] The candidate has a strong profile for this role, with proven experience in Python, deploying AI/ML models, and leadership. The ATS score is high due to the strong alignment with core AI/ML skills. The only minor deductions are for the lack of specific AWS service experience (Sagemaker/Bedrock), which is mitigated by his experience with other major LLM APIs. **Strengths:** Deploying models to production, Python, AI/ML Engineering

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] The candidate has some relevant skills: Python, Linux, PostgreSQL, data pipeline concepts, and Git. However, this Specialist Data Engineer role requires deep expertise in Big Data tools (Spark, Kafka, Airflow, AWS EMR, Databricks), cloud IaC (Terraform), MLOps (MLflow), and streaming/batch pipeline management at enterprise scale. The candidate's pipeline experience is at small scale (single server). AWS fundamentals vs required hands-on cloud infrastructure is a significant gap. The LLM/Vector Store knowledge matches the GenAI desirable requirement. **Strengths:** Python programming, Linux knowledge, LLM and Vector Store knowledge (GenAI requirement), PostgreSQL/SQL **Critical Gaps:** No Big Data engineering experience (Spark, Kafka, Airflow, Databricks) — fundamentally different domain **Missing Required:** Cloud infrastructure hands-on (AWS/GCP), Spark/Kafka/Airflow, Databricks, Terraform, MLflow/MLOps, Big Data pipeline experience at scale

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This role requires deep GCP expertise across numerous specific services (Compute Engine, GKE, Cloud Run, BigQuery, Vertex AI, Pub/Sub, Dataflow, Dataproc), Kubernetes on GKE, Terraform, ML/AI frameworks (TensorFlow, PyTorch), and Stackdriver monitoring. The candidate has zero GCP experience, no Kubernetes experience, no Terraform experience, and no TensorFlow/PyTorch experience. The only adjacency is general cloud concepts (AWS fundamentals), Docker, and some AI API usage. This is a deep cloud architect role in a completely different ecosystem than the candidate's experience. **Strengths:** Docker (basic), English fluent (required), AI API experience (tangential) **Critical Gaps:** GCP ecosystem (completely absent), Kubernetes orchestration, ML frameworks (TensorFlow/PyTorch) **Missing Required:** GCP cloud native architecture, Kubernetes/GKE, Terraform, GCP services (Vertex AI, BigQuery, Pub/Sub, etc.), TensorFlow or PyTorch, Cloud Monitoring/Stackdriver, Cloud security/IAM/VPC

[ANALYSIS_REPORT] **MEDIUM** [2.5-Pro] The candidate aligns well with the AI/ML and architecture aspects of the role, including Python, MLOps, and LangChain. The score is capped at 70 because of a significant gap in a required technology: Snowflake. Additionally, the ATS will likely penalize him for not meeting the '8+ years' of experience in a specific AI/ML architect role, even though his overall career is longer. **Strengths:** AI/ML Architecture, LangChain, Python **Missing Required:** Snowflake (Cortex, Vector Search), 8+ years of professional experience in AI/ML architecture

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] Senior DevOps role requiring 5+ years in cloud operations/DevOps/SRE, strong AWS proficiency, Terraform, Kubernetes, Databricks, and ML workload support. The candidate has Docker, Python, Bash, Linux, CI/CD concepts, and some AI/ML integration experience but lacks the core DevOps stack depth: no 5+ years DevOps experience, no strong AWS proficiency, no Terraform, no Kubernetes, no Databricks. The ML workload support and Databricks requirements are completely absent. Remote role at BIX (smaller company) with advanced English requirement (candidate meets). **Strengths:** Python and Bash scripting, Docker experience, Linux proficiency, English advanced (required), AI/ML concepts **Critical Gaps:** No DevOps/SRE/Cloud Ops role experience (5+ years required) **Missing Required:** 5+ years cloud ops/DevOps experience, Strong AWS proficiency, Terraform/IaC, Kubernetes, Databricks, CI/CD tools at depth, APM/monitoring tools

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-6] This is an affirmative action position explicitly for women ('Vaga afirmativa para mulheres'). The candidate is male, which is a demographic hard filter. Beyond this, the role requires ML/MLOps experience, Python with ML frameworks, AWS AI services, and technical leadership in AI/ML — areas where the candidate has some but not deep experience. However, the demographic filter makes all other analysis moot. **Critical Gaps:** Demographic requirement: affirmative action for women **Missing Required:** Female gender (affirmative action position)

[ANALYSIS_REPORT] **LOW** [2.5-Pro] The ATS score is very low due to a critical gap in the required technology stack. The role is explicitly for an Azure-focused DevOps engineer, and the candidate has no listed experience with Azure, Azure DevOps, or PowerShell. His skills in Python and conceptual understanding of pipelines are not enough to overcome the lack of platform-specific knowledge. **Strengths:** Python, Understanding of ML pipelines, Systems thinking **Critical Gaps:** Azure **Missing Required:** Azure DevOps, Infrastructure as Code, PowerShell, Experience with Azure