Top Data Engineer Jobs in Sao Paulo

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.

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.

Develop and maintain scalable cloud data pipelines and infrastructure for analytics and machine learning solutions. Collaborate across teams to ensure data integrity, usability, and quality; document workflows and architectures; build CI/CD pipelines for ETL processes; and troubleshoot data-related issues. The role requires Databricks, Spark, Python, SQL, dimensional modeling, and data lake or warehouse implementation experience.

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.

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.

Build and maintain dbt models in a production Snowflake data platform, implement entity resolution and AI-powered tagging, write data quality tests, maintain source documentation and lookup tables, monitor dbt Cloud jobs, debug data issues, and submit documented pull requests. The role requires strong SQL, dbt, cloud data warehouse, dimensional modeling, Git workflows, and remote collaboration skills.

Build machine learning systems that detect and de-identify sensitive information across text and image data. Develop data pipeline validation, adversarial test sets, regression gates, replacement pipelines, and audit processes. Measure precision, recall, leak rates, false negatives, and replacement consistency while investigating data quality issues across pipeline stages. The role requires strong Python, SQL, ML evaluation, privacy compliance knowledge, and experience with NER or similar detection systems.

Designs, develops, and supports scalable data pipelines, integrations, data models, warehouses, and enterprise data products. The role integrates internal, cloud, API, and vendor data while ensuring quality, governance, performance, and reliability. Responsibilities include stakeholder collaboration, technical leadership, Agile project support, vendor partnership, and continuous improvement of data architecture and platform capabilities.

Build and own an enterprise lakehouse data platform from the ground up, including architecture, ingestion, transformation, entity resolution, pipelines, infrastructure as code, and platform services. Establish engineering standards, mentor team members, and ensure the platform supports analytics, machine learning, and business intelligence. Partner directly with client leadership to translate business needs into technical solutions and present architectural decisions.

Design and optimize application database ecosystems across SQL Server and MongoDB. Responsibilities include schema and data modeling, API-to-database integration, performance tuning, troubleshooting data quality and consistency issues, reviewing and deploying database changes, and implementing testing and quality standards. The role collaborates with engineering and product teams and may support Azure data pipelines using Python and related technologies.

Senior Data Engineer serving in a part-time advisory capacity to audit AWS architecture, CI/CD, SDLC governance, data pipelines, and MLOps infrastructure. The role evaluates ETL architecture and AI-assisted development workflows, recommends technical improvements, mentors infrastructure engineers, and reports risks and readiness to leadership.

Audit AWS architecture, CI/CD pipelines, SDLC governance, and ETL/data-ingestion architecture for a media analytics platform. Evaluate AI-assisted development workflows, MLOps infrastructure, and engineering quality and cost governance. Provide technical recommendations, assess risks and readiness, and mentor the infrastructure and engineering team in a part-time advisory capacity.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, ingest Amazon DSP and other digital advertising data, evaluate managed ELT tools versus custom infrastructure, and design AWS-based pipelines supporting client dashboards. Collaborate with backend, infrastructure, and data science teams to align ingestion with attribution and predictive scoring needs.

Build and manage scalable open-source data infrastructure on Amazon EKS. Deploy technologies including Trino, Spark, Pinot, and Druid; implement infrastructure as code with Terraform and Helm; troubleshoot complex customer environments; and design novel, production-ready solutions using Kubernetes, AWS, and broad open-source technologies.

Designs and deploys secure AWS cloud architectures, leads enterprise migrations and application modernization, and builds Generative AI and machine learning solutions using Bedrock, SageMaker, vector databases, and RAG. Acts as the primary technical leader for client engagements, advises executive stakeholders, develops proofs of concept, drives AWS adoption and account growth, and mentors engineering teams while creating reusable infrastructure and delivery frameworks.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Responsibilities include complex data transformations, performance and cloud cost optimization, troubleshooting pipeline bottlenecks, enforcing data quality and governance standards, and collaborating with technical and business teams to deliver production-grade data solutions.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Develop complex data transformations, improve performance and cloud cost efficiency, troubleshoot processing bottlenecks, and enforce data quality and governance standards. Collaborate with engineering and business teams to deliver production-grade data solutions and communicate technical concepts clearly.

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.

Own and evolve databases, storage, networking, and Kubernetes infrastructure as platform products. Responsibilities include provisioning, backups, upgrades, capacity planning, performance tuning, migrations, incident leadership, root-cause analysis, on-call participation, and roadmap ownership. The role requires operating high-throughput ClickHouse or comparable OLAP systems, Ceph or S3-compatible storage, self-managed Kubernetes on bare metal, Linux performance debugging, and production networking expertise.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

Build and govern Databricks data foundations, including ingestion pipelines, entity resolution, history-aware data models, certified tables, data quality, lineage, access controls, and freshness monitoring. Develop APIs, webhooks, and MCP tools that enforce permissions and provide traceable data access. Collaborate with architects, AI and full-stack engineers, business stakeholders, and client teams while documenting and handing over solutions.

Own end-to-end data engineering for a Databricks-based commercial intelligence platform. Build entity resolution and reconciliation systems, evolve data models, onboard sources, and implement testing, monitoring, alerting, lineage, and incident analysis. Productionize data science models, analyze data quality and roadmap priorities, and provide technical leadership through code reviews. The role requires strong SQL, Python, PySpark, data modeling, orchestration, CI/CD, and hands-on Databricks experience.