Top Data Engineer Jobs in Sao Paulo

Designs, develops, tests, and maintains scalable data pipelines and ETL/ELT solutions across cloud platforms, warehouses, and lakehouses. Profiles and validates source data, implements quality controls, monitoring, lineage, error handling, and automated testing, and optimizes SQL and stored procedures. Investigates pipeline failures, resolves data issues, documents technical processes, and collaborates with data owners, architects, security teams, and platform engineers while following secure data handling practices.

Design and build scalable cloud-based data solutions, develop ETL pipelines for data warehouses and lakes, ensure data quality, optimize storage and retrieval, and collaborate with data architects, modelers, product managers, analysts, and stakeholders. The role also guides junior data engineers and applies AWS, Databricks, Python, PySpark, SQL, Airflow, and data modeling technologies.

Develop and maintain scalable data pipelines, integrations, data models, warehouses, and enterprise data products. Support AWS-based data platforms, data quality, governance, query optimization, and troubleshooting. Collaborate with technical and business stakeholders to translate requirements into reliable data solutions, participate in Agile delivery, and contribute to platform improvement initiatives.

Design, develop, maintain, and optimize scalable data pipelines using Snowflake, Databricks, and AWS. Build data integration and transformation solutions, monitor workflow performance, collaborate with analysts and stakeholders, participate in code reviews, document processes, and follow data engineering best practices.

Owns the operational backbone of a cloud data platform, including Azure and Snowflake provisioning, Terraform infrastructure, Azure DevOps CI/CD, monitoring, security, cost governance, CDC replication operations, and production readiness. The role also develops runbooks, manages access and secrets, coordinates infrastructure dependencies, conducts rollback rehearsals, and leads progressive knowledge transfer with client DataOps and security teams.

Build and maintain data pipelines, integration scripts, AI-powered workflows, and automation systems using Databricks, SQL, Python, and Git. Collaborate with nontechnical stakeholders to translate operational needs into technical solutions, monitor data integrity, improve workflow reliability, and eliminate manual processes through automation and AI-assisted development.

Build and maintain scalable data pipelines using Python and Airflow; transform, model, and validate data for downstream analytics users. Implement monitoring, logging, alerting, testing, and quality controls. Optimize cloud data systems for performance and cost, contribute to architecture and infrastructure-as-code decisions, and document processes. Collaborate with analytics engineers, analysts, and data scientists while supporting reliable enterprise data warehouses.

Owns credit bureau furnishing operations and the data pipelines supporting accurate customer account reporting. Responsibilities include building and debugging SQL and Python workflows, investigating discrepancies, resolving reporting defects, performing reconciliations, developing tests and monitoring, and partnering with Customer Service, Compliance, and Engineering. The role requires documenting data lineage, translating reporting requirements into technical rules, and ensuring verified, repeatable corrections.

Own credit bureau furnishing operations and the data pipelines supporting them. Build and debug SQL transformations, Python workflows, and reporting processes; investigate discrepancies, resolve defects, reconcile outputs, and implement automated tests, monitoring, and alerts. Partner with Customer Service, Compliance, and Engineering to address disputes, translate reporting requirements, validate fixes, and document data lineage and procedures.

Develop and optimize scalable data pipelines, ETL/ELT processes, data architectures, integrations, and migration solutions using Databricks and AWS. Responsibilities include migrating notebooks, jobs, pipelines, storage, tables, streaming checkpoints, and secrets; implementing data quality, governance, security, and validation controls; optimizing performance and costs; documenting solutions; and collaborating with analytics managers and business stakeholders in an Agile pod.

Own and improve a Snowflake-based enterprise data platform in a regulated healthcare environment. Responsibilities include operating Snowflake, ingestion pipelines, dbt models, AWS S3 workflows, Argo and Kubernetes workloads, data quality, observability, access controls, incident resolution, Tableau dependencies, automation, cost optimization, documentation, and mentoring. The role includes senior technical ownership, stakeholder collaboration, controlled change processes, and participation in an on-call rotation.

Develop and maintain Tableau and Power BI dashboards, reports, semantic models, and enterprise data integrations. Partner with stakeholders to gather requirements, define KPIs, model data, troubleshoot reporting pipelines, and improve data quality and governance. Work with Snowflake, SAP HANA, and SAP-based sources while documenting solutions and supporting scalable analytics in an Agile, distributed environment.

Build and maintain Tableau and Power BI dashboards, onboard enterprise data from Snowflake and SAP systems, and translate business requirements into scalable data models, KPIs, and reporting solutions. Partner with business and technical stakeholders to improve data quality, governance, consistency, and visualization. Troubleshoot issues across source systems, data platforms, semantic models, and reporting layers while documenting technical solutions and working in an Agile environment.

Own and improve a Snowflake-based enterprise data platform in a regulated healthcare environment. Responsibilities include operating Snowflake, managing ingestion through Fivetran, HVR, and custom pipelines, developing dbt models, supporting AWS S3 and Kubernetes workflows, improving data quality and observability, resolving incidents, administering access, supporting Tableau dependencies, documenting procedures, and mentoring engineers.

Designs, builds, and maintains scalable ETL/ELT pipelines, data models, storage solutions, and data workflows, primarily using AWS. Ensures data integrity, governance, security, observability, reliability, and performance across structured and unstructured data systems. Collaborates with product, engineering, analytics, and machine learning teams to support data-driven initiatives and optimize analytical and operational data infrastructure.

Build and maintain scalable ETL/ELT pipelines, data transformations, models, integrations, and cloud-based data platforms. Ensure data quality through testing and monitoring, troubleshoot performance issues, collaborate with technical and business stakeholders, participate in code reviews, document solutions, and improve platform reliability and efficiency.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and scale data pipelines and cloud infrastructure supporting analytics, machine learning, and generative AI. Integrate marketing data from GA4, advertising platforms, CRMs, and CDPs; establish data quality, reliability, and engineering standards; collaborate with ML engineers; improve processes; mentor engineers; and deliver product-minded solutions using AI-enabled development workflows.

Own FinOps and data initiatives focused on optimizing Databricks workloads, pipelines, clusters, storage, and cloud costs. Build executive dashboards, monitor spending, detect anomalies, establish governance, and improve showback, chargeback, forecasting, and unit economics. Partner with engineering, finance, product, cloud, and operations teams to guide cost-efficient architecture and ROI-based decisions.

Investigate and resolve enterprise data incidents using SQL and MS SQL Server; monitor Airflow, Azure Data Factory, and SQL Agent pipelines; troubleshoot Tableau dashboards and data warehouses; perform controlled data corrections; automate investigations with Python or PowerShell; and coordinate incident resolution through Jira Service Management with engineering, IT, and business teams.

Build and maintain high-volume ETL/ELT pipelines using Python, SQL, Airflow, Azure Data Factory, and SQL Server/PostgreSQL. Design data ingestion, metadata, chunking, and embedding pipelines for AI and RAG systems. Optimize queries, dimensional models, warehouses, and lakehouses while enforcing data quality and governance. Collaborate with product owners, data scientists, and IT Operations to resolve production issues and maintain Git, Jira, and CI/CD standards.

Build semantic models, analytical views, and production ETL/ELT pipelines; optimize SQL queries and schemas; implement governance, security, data quality, monitoring, and anonymization; and support AI, RAG, and agentic AI workflows. The role requires strong production data engineering experience, dimensional modeling, dbt, database security, query tuning, and advanced English for direct client collaboration.

Build and operate production ETL/ELT pipelines connecting Veeva or other CRM systems, Data Lake, Databricks, and Adobe Experience Platform. Model analytical data, calculate engagement indicators, implement automated quality testing and reconciliation, and support XDM and identity strategies. Apply LGPD through minimization, pseudonymization, retention, deletion, and audit controls. Document lineage and runbooks, monitor SLAs, and investigate production incidents.

Develops data integration and analytics solutions for financial loss reporting. Partners with stakeholders, product owners, SMEs, and technology teams to document requirements, validate business rules, support source-to-target mapping and UAT, certify data products, resolve data quality issues, and improve dashboards, reports, data platforms, and downstream data consumption.

Designs and maintains scalable data pipelines and infrastructure for analytics and machine learning. Responsibilities include ETL CI/CD, dimensional modeling, Data Lake and Data Warehouse implementation, data governance, data quality, documentation, and technical troubleshooting. Requires Databricks, Spark, Python, SQL, Azure DevOps, and Azure Data Factory experience, plus advanced English. Power BI, Unity Catalog, SAP, FinOps, and Databricks certification are preferred.