Anti-ATS Evaluatorv1.3

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The candidate matches the AI and LLM aspects of the role reasonably well and has some CI/CD and architectural experience, but the job explicitly requires mastery of CI/CD, cloud, containerization, DevSecOps, and several years of coaching/training experience, which are not clearly demonstrated. This creates significant gaps between the advertised level (architect + coach across cloud-native, secure systems) and the primarily systems/ops and AI-infra profile. To get closer to this bar, the candidate would need concrete examples of leading CI/CD and DevSecOps initiatives at scale and evidence of repeated coaching or facilitation work with engineering teams. **Strengths:** Strong AI/LLM and prompt-engineering experience in production systems, Experience defining architectures and constraints from the metal up, Comfort translating complex technical topics for non-technical stakeholders **Missing Required:** 6+ years in software development at the level expected of a coaching architect, 3+ years in formal training/coaching roles, Mastery of CI/CD, cloud, containerization, and DevSecOps across organizations

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] While the candidate has strong experience architecting AI-powered automation systems, this role is tightly centered on the Microsoft Copilot, Power Platform, Azure AI, and Databricks ecosystem, which is not reflected on the resume. As a result, ATS will see multiple missing required technologies around the Microsoft stack and enterprise automation CoEs. To improve for this kind of role, the candidate would need concrete project work using Copilot Studio, Power Automate, Azure AI services, and ideally Databricks or similar enterprise data platforms. **Strengths:** Proven ability to design and deliver AI-driven automation solutions in production, Strong architectural thinking and focus on reliability, cost, and constraints, Experience leading small-scale AI and automation initiatives end-to-end **Missing Required:** Hands-on expertise with Microsoft Copilot and Power Platform AI capabilities, Experience integrating automation solutions with Databricks or similar enterprise data platforms, 7+ years of enterprise solution/automation architecture centered on Microsoft technologies

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] Candidate has Python, SQL, and data pipeline concepts but lacks GCP-specific tools (BigQuery, Dataflow, Composer), PySpark, DBT, and enterprise data engineering experience. No Airflow or DataStage. Data pipeline from scratch exists but not at enterprise scale. To improve: Would need GCP certification and PySpark training. **Strengths:** Python, SQL, PostgreSQL, Pipeline architecture thinking **Critical Gaps:** No enterprise data engineering experience **Missing Required:** PySpark, GCP data services, DBT

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] JD requires Azure/Databricks expertise, PySpark, Delta Lake, Data Factory - candidate has none of these specific technologies. Python and SQL present but not in data engineering context. No Azure certifications which are listed as requirements. To improve: Would need Databricks training and Azure certification path. **Strengths:** Python basics, SQL, Data quality mindset **Critical Gaps:** No Azure ecosystem experience, No Databricks **Missing Required:** Azure services, Databricks platform, DP-203/DP-700 certifications

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] The candidate has strong SQL, data pipeline, and AI/LLM familiarity, which partially matches the data engineering and AI aspects of the role. However, there is no evidence of Snowflake, Snowflake Cortex, semantic models, or dedicated data engineering titles, which are central to this position. To improve the match, the candidate would need to acquire and highlight hands-on Snowflake projects (including AI features) and describe work that resembles semantic modeling and data abstraction layers. **Strengths:** Strong SQL and data manipulation ability across ERP and job intelligence systems, Hands-on LLM and prompt engineering experience that aligns with AI-guided analytics, Proven history of translating business context into structured data logic **Missing Required:** 5+ years of professional experience specifically as a Data Engineer, Hands-on Snowflake experience, including AI-related features

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opus-4-5] Vague JD with minimal requirements specified - just 'Data Engineer' for 'Data and AI solutions'. Candidate has data pipeline experience, automation focus, and AI integration. Recife location may be hybrid requirement. Consulting firm vibes (complex situations to positive results). To improve: Emphasize data transformation projects and AI automation. **Strengths:** Data automation experience, AI integration, Business outcome focus

[ANALYSIS_REPORT] **MEDIUM** [Opencode: opus-4-5] Candidate has SQL, Python basics, and business-data translation skills. Feature engineering concepts from trading work transfer. However, lacks Spark, AWS-specific experience, R, and banking domain. 'Data translator' aspect aligns with bridge skills. To improve: Add Spark basics, emphasize feature creation from trading strategies. **Strengths:** SQL proficiency, Python, Business-data translation, Feature engineering concepts (trading) **Missing Required:** Spark experience, AWS knowledge

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The candidate brings solid SQL, Python, and ETL-style experience plus a strong understanding of operational data, but lacks explicit exposure to AWS data stacks (S3, Redshift, Athena), star schemas, and formal data warehousing practice. The JD strongly emphasizes traditional data engineering patterns and tooling, which are not demonstrated in the resume. To improve, the candidate would need practical projects in AWS-based data warehousing (Redshift/Athena), star schema modeling, and perhaps dbt/Airflow to align with expectations. **Strengths:** Robust SQL and Python background with real-world ETL-like work, Experience building automation around transactional and operational data, Use of Git and structured processes in other engineering contexts **Missing Required:** 4+ years of experience as a Data Engineer (title and responsibilities), Strong background in Data Warehousing and Data Lakes using modern cloud tooling

[ANALYSIS_REPORT] **LOW** [Opencode: opus-4-5] JD requires solid Databricks Platform and Apache Spark/PySpark experience - candidate has neither. Delta Lake, cloud Data Lakes (ADLS/S3), and Spark optimization are core requirements with no demonstrated experience. Python present but not in data engineering context. To improve: Not viable without Databricks/Spark training. **Strengths:** Python basics, SQL, Data quality awareness **Critical Gaps:** No Databricks experience, No Spark experience **Missing Required:** Databricks Platform, Spark/PySpark, Delta Lake

[ANALYSIS_REPORT] **LOW** [3-Flash] The candidate has strong SQL and Python skills and experience with ERP systems, which are relevant for the pipeline development. However, the lack of Spark, Airflow, and the mandatory 'Fluency in Spanish' requirement creates a critical gap for this LATAM-focused role. To improve, the person should highlight any exposure to big data tools or Latin American market projects. **Strengths:** Advanced SQL, Python for data scripts, ERP integration experience **Critical Gaps:** Mandatory Spanish requirement not met, Big Data (Spark/Databricks) **Missing Required:** Espanhol Avançado ou Fluente, Big Data Spark Databricks

[ANALYSIS_REPORT] **LOW** [3-Flash] The person demonstrates high proficiency in SQL and Python but lacks specific experience with the dbt/BigQuery stack and Kimball modeling mentioned in the JD. The candidate's background is more focused on backend automation and infrastructure than pure Analytics Engineering. Bridging this gap requires showcasing projects involving dimensional modeling and warehouse orchestration tools. **Strengths:** Advanced SQL, Python, CI/CD and Git **Critical Gaps:** Specific Analytics Engineering stack (dbt/BigQuery) **Missing Required:** dbt experience, BigQuery/AWS solutions, Dimensional modeling

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] There is a strong match in infrastructure-as-code concepts and vector databases, which aligns with the candidate's recent work in AI pipelines. However, the role requires specific AWS big data tools (Athena, Glue, Spark) and streaming (Kafka) that are missing. To improve the score, the candidate should demonstrate how their custom Go orchestrator handles similar distributed data challenges. **Strengths:** Vector Databases (Milvus/Pinecone context), Advanced SQL/PostgreSQL, Systems Architecture **Critical Gaps:** Big Data streaming and processing (Kafka/Spark) **Missing Required:** Spark, Kafka, AWS Big Data ecosystem

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] This position requires deep Databricks, Apache Spark, Java-based integration services, and event-driven AWS architectures, none of which appear in the candidate’s background. While the candidate has Python, SQL, and LLM-related data work, the stack and focus here are large-scale payroll analytics and Spark/Databricks MLOps. To approach this kind of role, the candidate would need to invest in Databricks/Spark, event streaming (SQS/SNS/Kafka), and Java service development experience. **Strengths:** Python and SQL experience with ETL-like logic in other domains, LLM feature engineering mindset from job intelligence systems, Attention to data quality and validation in operational settings **Missing Required:** Strong experience with Databricks platform and Apache Spark/PySpark, Hands-on experience with event-driven architectures on AWS (SQS/SNS/Kafka), Java development experience for building integration services at scale

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] This job centers on deep SQL Server expertise, T-SQL performance tuning, high-availability configurations, and AWS/Terraform-based data platform engineering, which are not present in the candidate’s history. While the candidate is strong in SQL, PostgreSQL, and Python pipelines, the required stack is that of a specialized SQL Server platform engineer in financial services. Bridging this gap would require hands-on SQL Server HA/TDE/RLS work and infrastructure-as-code experience, which go beyond the current profile. **Strengths:** Solid SQL and Python skills for data manipulation, Experience designing data pipelines oriented around business decisions, Comfort working with finance-related data and operational analytics **Missing Required:** Deep SQL Server expertise including HA and advanced T-SQL performance tuning, Experience managing AWS-based data infrastructure using Terraform or CDK, Experience with Airflow-driven ETL/ELT and DataOps best practices

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] The candidate matches Python, SQL, and Bash scripting well and has meaningful ETL-like experience, but lacks explicit exposure to Databricks, Spark, Fivetran, dbt, and specific cloud data platforms that define this associate data engineering role. ATS will therefore see a partial match suitable for junior data work but not a clean tooling fit. To improve alignment, the candidate should highlight any Spark-like work, mention at least one cloud data platform, and add targeted learning or small projects using Databricks, dbt, or similar tools. **Strengths:** Strong Python and SQL skills used to build real operational systems, Experience designing ETL-like processes in ERP and e-commerce contexts, Comfort with automation and Bash scripting for scheduled jobs

[ANALYSIS_REPORT] **LOW** [3-Flash] The candidate has the core SQL and modeling logic, but the job is strictly centered on the Azure/Databricks ecosystem, which is a major gap. The 'Senior' requirement usually demands deep, platform-specific experience that the candidate's self-taught/Go-heavy background doesn't provide. Highlighting the Mechatronics background for logic and English for communication could help, but the stack mismatch is severe. **Strengths:** Advanced SQL, English fluency, Systems integration **Critical Gaps:** Azure/Databricks platform experience **Missing Required:** Azure Data Factory, Databricks, Azure Cloud experience

[ANALYSIS_REPORT] **MEDIUM** [Copilot: GPT5.1] This analytics engineer role strongly fits the candidate’s business-technical bridging, KPI definition, and SQL-heavy modeling experience, but the specific stack (BigQuery, Dataform, Medallion, Airflow/Composer) is missing. ATS will give credit for SQL, analytics, and KPI design but penalize for the absent GCP and modern analytics engineering tools. To better match, the candidate should showcase any cloud data work, approximate Medallion-like modeling or dimensional schemas, and consider a small BigQuery/Dataform project to anchor the right keywords. **Strengths:** Proven ability to define KPIs and build operational reporting from messy ERP data, Strong SQL skills applied to forecasting, data remediation, and operational analytics, Clear experience serving as a bridge between business stakeholders and technical teams **Missing Required:** Hands-on analytics engineering on BigQuery, Experience implementing Medallion Architecture and dimensional models in a cloud warehouse

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The candidate matches the IA focus and the need for high-quality datasets, given their recent AI scoring pipeline project. The primary gaps are the specific banking data experience and the Databricks/Spark stack. To improve, the person should link their 'Supply Chain Analytics' and 'Finance' background to the 'Wholesale/Banking' data requirements. **Strengths:** Python for Data Engineering, IA/LLM dataset preparation, Financial background **Critical Gaps:** Databricks/Spark ecosystem **Missing Required:** Databricks, Spark, Banking-specific data experience

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The role values the ability to translate business needs into technical solutions, a core strength of the candidate. However, it requires an advanced level of pySpark and specific AWS data services (Glue, EMR) that are not prominent in the resume. Highlighting the automation of the international e-commerce supply chain would demonstrate the required 'data-driven' culture. **Strengths:** Bridge between business/tech, Python/SQL proficiency, DataOps mindset **Critical Gaps:** Distributed processing (pySpark) **Missing Required:** pySpark, AWS Data Services

[ANALYSIS_REPORT] **TOP** [Copilot: GPT5.1] This role is an excellent match: the candidate has strong AI/LLM skills, ETL and data integration experience, ERP and technical project backgrounds, and is a native Portuguese speaker, all highly relevant to building AI solutions for engineering projects and SQL Server integrations. The main missing elements are specific experience with Autodesk Docs and explicit SQL Server mention, but the underlying skills (SQL, ETL, integrations, automation) are clearly present. To further improve, the candidate should highlight any work with CAD/engineering artifacts or similar document processing and mention SQL Server explicitly if possible. **Strengths:** Strong applied AI and automation experience aligned with internal assistants and workflows, Deep background in engineering, control systems, and ERP data integration, Native Portuguese and Brazil-based, fitting the language and local context implicitly

[ANALYSIS_REPORT] **LOW** [3-Flash] While the candidate has the required English fluency and senior-level thinking, the technical stack is entirely Azure-based, where the candidate has little exposure. The role involves mentoring, which the candidate has done as a 'Sócio-Fundador', but the platform gap is significant. Emphasizing the Mechatronics degree could show the 'Computer Science equivalent' required. **Strengths:** English Fluency (C2), Mentorship/Leadership experience, Senior analytical capability **Critical Gaps:** Microsoft Azure platform expertise **Missing Required:** Azure stack, Azure DevOps

[ANALYSIS_REPORT] **TOP** [3-Flash] This is a strong potential match because it blends Data Engineering with GenAI/RAG, which is the candidate's recent area of focus. The missing keywords are the specific AWS services (Glue, Bedrock, SageMaker) and Infrastructure-as-Code. To reach a top score, the person needs to frame their custom scoring pipeline within the context of MLOps/DataOps. **Strengths:** GenAI/RAG experience, Prompt Engineering/LLM evaluation, Python/SQL for Data Pipelines **Critical Gaps:** AWS-specific ML/Data services (SageMaker/Glue) **Missing Required:** Glue, SageMaker, Terraform

[ANALYSIS_REPORT] **MEDIUM** [3-Flash] The candidate has deep understanding of RAG and prompt engineering, which are central to this role. However, the requirement is heavily tied to the Microsoft ecosystem (Copilot Studio, M365, Power Platform), which is not in the candidate's background. To improve, the candidate should highlight their experience with Google Gemini/OpenAI as a direct transferable skill for LLM orchestration. **Strengths:** RAG architecture, Prompt Engineering, English Fluency **Critical Gaps:** Microsoft AI ecosystem **Missing Required:** Copilot Studio, Azure AI Foundry, Microsoft Graph API

[ANALYSIS_REPORT] **LOW** [Copilot: GPT5.1] The candidate brings strong AI/LLM, automation, and some statistical/analytics background, but the role demands deep machine learning, neural networks, and a CS/AI-centric profile, which are not clearly demonstrated. There is some overlap with pattern recognition and NLP through LLM pipelines, yet classic ML engineering experience is thin. To better fit, the candidate would need to show concrete ML models (beyond LLMs), neural network work, and more traditional ML deployment experience. **Strengths:** Significant experience designing and deploying LLM-based systems and NLP-style pipelines, Cross-industry exposure to financial and operational analytics use cases, Strong systems and software design skills for production-ready AI solutions **Missing Required:** Strong expertise in Machine Learning, Pattern Recognition, and Neural Networks, Bachelor's or advanced degree in Computer Science, Artificial Intelligence, or closely aligned field as recognized by ATS

[ANALYSIS_REPORT] **TOP** [3-Flash] This is an exceptional match. The candidate has direct experience with Global Markets (Trading, Crypto arbitrage) and has built LLM-based tools for non-structured data (scrapers, scoring). The only minor gap is 'Databricks', but their 'metal' engineering approach to scrapers is exactly what a senior role in a high-stakes environment needs. No modifications needed other than highlighting the financial trading results. **Strengths:** Global Markets/Trading experience, Unstructured data (LLM/NLP) expertise, Senior/Founder autonomy