Top Data Engineer Jobs in Brazil

Designs and deploys scalable data pipelines and ETL/ELT processes using Azure and Databricks. Develops Python, PySpark, and SQL solutions, builds relational and analytical data models, optimizes pipeline performance, and ensures data quality. Serves as a technical SME for vendor developers through code reviews, quality standards, delivery oversight, and collaboration with product owners and business stakeholders.

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.

Accelerate business definition capture and certification in Databricks Unity Catalog. Collaborate with data stewards and domain owners across Finance, Commercial, HR, and Marketing; facilitate workshops; resolve terminology conflicts; improve Python and Excel intake processes; validate and upload approved definitions; establish lightweight governance; and document processes for handoff.

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.

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 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 and evolves petabyte-scale AWS data platforms and resilient data pipelines using JVM languages, Spark, Python, and cloud-native services. Leads architecture, distributed systems design, query optimization, testing, monitoring, and production readiness. Provides technical mentorship, code reviews, and cross-team leadership while delivering complex initiatives in agile environments. Explores GraphQL, vector databases, and AI/LLM techniques to improve data access and platform efficiency.

Build and operate a governed people-data platform supporting workforce analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, privacy controls, and reliability practices. Own the platform’s AI-readiness and infrastructure roadmap, monitor performance, manage incidents, and partner with People Analytics, HR Technology, Security, Legal, and Enterprise Data teams. Translate approved requirements into secure, production-ready technical solutions using Workday and Azure-based data technologies.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon and DSP APIs, design scalable AWS-based data pipelines, evaluate managed ELT solutions, and support application and backend work. Collaborate with infrastructure and data science teams to align ingestion outputs with attribution, scoring, dashboards, and predictive models.

Build and own an AWS-based ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon API and DSP data sources, evaluate Fivetran versus custom infrastructure, and align outputs with dashboard, attribution, and scoring requirements. Provide broader backend and application engineering support, collaborate with infrastructure and data science teams, and operate within existing ECS, S3, and RDS environments.

Build and own an AWS-based 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 align pipeline outputs with dashboards, attribution logic, and downstream models. Provide additional application and backend engineering support while collaborating with infrastructure and data science teams.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, ingest Amazon API and DSP data, evaluate Fivetran versus custom infrastructure, and design pipelines powering client dashboards. Work within AWS using ECS, S3, and RDS, support backend application needs, and coordinate with data science teams to align ingestion outputs with attribution and scoring models.

Replace a third-party data ingestion tool with an owned ETL pipeline using AWS services. Document legacy ingestion logic, build pipelines integrating Amazon API and DSP data, evaluate custom infrastructure versus managed ELT tools, and ensure outputs support dashboards, attribution, and scoring models. Provide broader backend and application engineering support, collaborate with infrastructure and data science teams, and work within the existing AWS environment.

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.

Replace a third-party data ingestion tool with an owned ETL pipeline. Document legacy ingestion logic, design and build scalable pipelines from Amazon DSP and other data sources, evaluate Fivetran versus custom infrastructure, and ensure outputs support dashboards, attribution, and scoring models. Provide additional backend engineering support within the existing AWS environment and collaborate with infrastructure and data science teams.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon DSP and other data sources, design schemas and transformations, evaluate managed versus custom solutions, and ensure data supports downstream dashboards, attribution, scoring, and predictive models. Provide additional backend and application engineering support within the existing AWS environment.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, ingest data from Amazon APIs and DSP platforms, design schemas and transformations, evaluate Fivetran versus custom infrastructure, and support AWS-based application and backend work. Collaborate with data science teams to align pipeline outputs with attribution, scoring, and dashboard requirements.

Replace a third-party ingestion tool with an owned ETL pipeline supporting Amazon DSP, other advertising platforms, and digital data sources. Document legacy ingestion logic, design schemas and transformations, evaluate Fivetran versus custom infrastructure, and build within AWS using ECS, S3, and RDS. Support application and backend work, coordinate with data science teams, and ensure pipeline outputs align with attribution, scoring, and dashboard requirements.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon API and DSP data sources, design schemas and transformations, evaluate Fivetran versus custom infrastructure, and align outputs with attribution and scoring models. Support application and backend engineering within AWS using ECS, S3, and RDS while collaborating with infrastructure and data science teams.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, integrate Amazon DSP and other digital advertising data sources, evaluate managed ELT tools, and align pipeline outputs with attribution, scoring, and dashboard requirements. Provide broader backend and application engineering support within AWS, collaborate with infrastructure and data science teams, and work with existing ECS, S3, and RDS environments.

Build and maintain secure HR data pipelines, warehouses, and data marts using dbt, Python, Snowflake, BigQuery, Airflow, and AWS. Design semantic layers and ontology models for BI and AI agents, deploy Snowflake Cortex and MCP workflows, monitor pipeline reliability, enforce data quality and PII security, and document technical processes. Collaborate remotely with distributed teams aligned to EST.

Build and maintain curated data layers for a finance data platform using SQL and Snowflake. Develop dimensional models, business transformations, Slowly Changing Dimensions, reconciliation checks, data-quality controls, and temporal data logic. Support BI consumers through governed data objects and participate in code reviews, CI/CD promotion, and automated testing. Collaborate in English with distributed engineering teams and US stakeholders.