Top Data Engineer Jobs in Rio de Janeiro

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouse and ETL workloads into a governed medallion Lakehouse architecture, optimize Spark and Delta performance, implement data quality and governance controls, troubleshoot production issues, and collaborate with DevOps and analytics teams. The role also involves testing, documentation, Agile delivery, mentoring junior engineers, and using AI development tools.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into a governed medallion Lakehouse, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate on observability, security, and compliance. Use AI coding tools to accelerate development. The role is fully remote and requires production-scale data platform experience.

Develop predictive and forecasting models, scalable data tools, dashboards, and executive reports to support revenue growth, operational efficiency, and strategic planning. The role partners with Finance, senior leaders, and business teams to translate complex data into actionable recommendations, define measurable outcomes, and lead analytics projects from requirements through presentation.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Owns end-to-end data engineering for ingestion, medallion architecture, curated datasets, and Power BI semantic layers. Builds scalable ETL/ELT pipelines, integrates ERP and CRM systems, implements data quality and governance controls, optimizes performance and cloud costs, and provides technical leadership. Requires Microsoft Fabric or Azure expertise, Azure Data Factory, advanced Power BI and DAX skills, data contract design, and experience with Bronze/Silver/Gold architectures.

Designs, builds, and maintains production data pipelines and infrastructure supporting analytics, machine learning, and financial decision-making. Responsibilities include data ingestion, modeling, quality validation, workflow orchestration, database optimization, monitoring, troubleshooting, and managing scalable AWS or on-premises systems. The role collaborates with data scientists and analysts to ensure reliable access to large-scale and time-series financial data.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Support Data & AI Governance initiatives through data analysis, curation, stewardship, documentation, cataloging, lineage, quality improvement, and policy maintenance. Use Python for analysis and automation while collaborating with technical and business stakeholders to improve data organization, accessibility, usability, and compliance with governance standards.

Design, develop, and maintain scalable cloud data pipelines and infrastructure for analytics and machine learning solutions. Collaborate with cross-functional teams to ensure data integrity, usability, and quality. Build CI/CD pipelines for ETL processes, document workflows and architectures, and provide technical troubleshooting. The role requires Databricks, Spark, Python, SQL, dimensional modeling, and Data Lake or Data Warehouse experience, with Azure, AWS, Power BI, and Unity Catalog as beneficial skills.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Lead the replacement of the TapClicks ingestion tool with a standalone ETL pipeline in AWS. Document legacy ingestion logic, evaluate Fivetran versus custom development, and ingest data from Amazon DSP and other programmatic platforms. Design pipelines supporting client dashboards, align outputs with attribution and scoring models, and provide broader backend support. The role requires strong SQL, large-scale data pipeline experience, AWS knowledge, and familiarity with digital advertising data.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, ingestion frameworks, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process telemetry and usage data. Optimize performance, reliability, and cost while ensuring data quality, validation, auditability, and production troubleshooting. Partner with Engineering and Finance stakeholders on usage-based insights and cost optimization initiatives.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers for marketing, client, BI, product, and AI consumers. Integrate fragmented advertising and customer data across multiple platforms and tenants, manage dbt projects, ensure data quality, optimize performance, and support client-specific modeling. Use AI-assisted development workflows, collaborate cross-functionally, and deliver reliable, AI-ready data products.

Manage and optimize PostgreSQL, SQL Server, and Oracle databases for performance, security, availability, and scalability. Design schemas, indexes, and partitions; oversee monitoring, backups, recovery, disaster planning, and compliance. Translate business needs into data engineering work, support Power BI solutions, lead standups, prioritize team workloads, mentor colleagues, and communicate with stakeholders. Apply AI and automation to database tuning, reliability validation, migration planning, testing, and operational efficiency.

The Data Engineer role involves developing and maintaining data pipelines, ensuring data quality, and collaborating with teams to deliver solutions tailored for financial services.

Own image and video dataset lifecycle and quality, build and maintain data pipelines and versioning, create internal tools for labeling and curation, coordinate with labeling teams, and improve training-data collection using model-assisted and active-learning approaches.

Lead data engineering architecture and standards, build and operate large-scale batch and streaming pipelines, partner with product and business teams to deliver scalable data solutions, manage technical debt, and balance build vs. buy decisions to maximize analytics and platform impact.

Design and maintain the graph data layer for a fashion advisory platform: schema design, batch ingestion, probabilistic entity matching and deduplication, editorial signal weighting, openCypher query optimization (sub-3s), and cross-functional collaboration with ML and GenAI agent developers.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Lead migration from SQL Server to AWS Redshift by developing production-grade dbt models, converting legacy T-SQL procedures into modular Redshift SQL, architecting dbt projects, optimizing batch transformations, and mentoring engineers on scalable data modeling and reporting practices.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

Design, build, and maintain ELT/ETL pipelines and modular dbt models in Snowflake. Implement automated data quality tests, enforce Git/CI-CD workflows, monitor platform operations, resolve pipeline incidents, collaborate with BI and data science teams, and document data lineage and cataloging standards for a multi-country fintech analytics platform.

Design and build multi-modal data ingestion pipelines, manage embeddings in vector and graph databases, integrate AI-assisted agents, collaborate on evaluation pipelines, and deploy/optimize scalable data services and workloads on GCP using Python-based APIs and orchestration tools.