Top Data Engineer Jobs in Rio de Janeiro

Operate and support ELT pipelines, design and maintain data warehouse and semantic-layer models, create BI data models and leadership reporting, troubleshoot data issues, prepare data for AI workflows, and collaborate with stakeholders to improve analytics infrastructure and recurring reporting processes.

Design and build scalable data pipelines and distributed processing systems on Databricks. Lead technical design, develop advanced data models, champion CI/CD and IaC, ensure operational reliability, mentor engineers, and collaborate cross-functionally to support analytics and ML use cases.

Design, build, and maintain scalable ETL/ELT pipelines and cloud-native Data Lake/Warehouse solutions on Azure. Implement data integration, quality, governance, dimensional models (SCD1/2), Python-based automations, CI/CD, and monitoring. Collaborate with international stakeholders to optimize performance, enable analytics, and support FinOps and deployment for enterprise data platforms.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

Lead design, build, and optimize a multi-country Snowflake data platform on GCP. Build CDC ingestion pipelines, implement dbt semantic models, manage Snowflake deployments via DCM/GitHub CI/CD, enforce governance/RBAC/PII protections, optimize FinOps/performance, and mentor data engineers while aligning stakeholders across Product, Risk, BI, and regional teams.

Own and scale the enterprise LookML semantic layer to deliver performant, reusable Looker data products. Translate stakeholder needs into durable models, optimize query performance, govern LookML across instances, mentor junior staff, and support conversational analytics and AI-driven queries.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Lead and mentor a data engineering team while owning end-to-end data platform architecture and delivery. Drive migration from Snowflake/SSIS to Azure Databricks and DBT, implement Terraform IaC, CI/CD, and scalable ETL/ELT pipelines, ensure data quality, observability, and platform stability, and collaborate with stakeholders across product, data, and business teams.

Design, build, and operate scalable data pipelines and lakehouse solutions using Azure Databricks, DBT, Snowflake and Python. Lead migrations, implement data quality and observability, support production SLAs, and mentor junior engineers while ensuring governance, cost optimization, and reliability.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design and build scalable ETL/ELT data pipelines and a cloud-based data warehouse/lakehouse. Improve data quality, governance, and documentation. Work with stakeholders to enable analytics, BI, and machine learning by delivering clean, analytics-ready datasets and establishing data engineering best practices.

Validate ETL/ELT pipelines and data migrations, test database objects, write complex SQL for verification, automate database regression tests, assess query performance, and validate BI reports in Tableau/Power BI while tracking defects in JIRA/TestRail.

Build and maintain scalable ELT/ETL pipelines and data models for cloud cost attribution and financial reporting. Design a Cloud Economics data warehouse, perform end-to-end validation and auditing, automate cost-optimization recommendations, and deliver high-quality datasets for Product, Engineering, and Finance stakeholders.

Design, build, and maintain high-performance backend microservices and automation for the GTM Data Platform. Implement RESTful APIs, webhook-driven lead routing, and Salesforce integrations. Support AI-driven sales workflows, maintain platform codebase, and collaborate in Agile/Scrum using JIRA with distributed teams aligned to EST.

Build the data foundation and LLM analysis layer for a Creative Engine: ingest and structure multi-platform performance and sales data, run video analysis with Gemini and Claude, implement semantic search with pgvector, connect signals into a queryable intelligence layer, integrate platform APIs, and own full-stack features (Django + HTMX + Tailwind) with async pipelines (Celery + Redis).

Design, build, and maintain cloud data pipelines and Snowflake/dbt data models; develop AI-powered applications using LLMs, RAG, tool-calling, and agent workflows; create APIs and integrations; monitor performance, data quality, and costs; troubleshoot interconnected systems; apply security, governance, testing, and CI/CD best practices; partner with stakeholders to deliver scalable solutions.

Design and optimise Snowflake architectures and customer data foundations to support segmentation, personalisation, and audience activation. Build pipelines and reverse-ETL flows to Braze, Hightouch, and CDPs, implement identity resolution and consent frameworks, ensure data quality and governance, and collaborate with CRM, analytics, and marketing teams to enable multi-channel customer engagement and scalable customer 360 solutions.

Owner of Databricks-based data platform spanning ingestion to dimensional models. Develop and maintain notebooks across medallion layers, implement SCDs and star schemas, manage deployments, monitor and troubleshoot production jobs, enforce code quality via reviews, improve CI/CD and testing, document pipelines, and mentor teammates while collaborating with client stakeholders on architecture and process improvements.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitor, optimize cost and performance, migrate legacy ETL, enforce Delta Lake and multi-tenant architectures, ensure data quality/security, support customer onboarding, and maintain runbooks and CI/CD for global, SLA-driven delivery.

Design, build, and maintain scalable ETL/ELT pipelines and cloud data architectures (Databricks, PySpark, modern warehouses). Ensure data quality, governance, performance, and integrations; collaborate with cross-functional teams; document solutions and apply data security and automation best practices.

Own quality for the Data Platform and Analytics products: validate reports and metrics with SQL, reconcile against raw sources/APIs, implement automated data quality checks (Soda), test Airflow DAGs and Databricks jobs, automate API tests (REST/GraphQL), and communicate findings to engineering, product, and analytics teams.

The role involves designing and optimizing distributed data systems, building real-time data pipelines, and mentoring engineers while focusing on scalable data architecture and AI infrastructure.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.