Top Data Engineer Jobs in Rio de Janeiro

Designs and governs data products for unstructured and semi-structured knowledge content in Databricks Unity Catalog. The role defines domain boundaries, data models, metadata taxonomies, lineage, access controls, and sensitivity classifications. It partners with data engineering, knowledge product, architecture, legal, privacy, and risk teams to support secure ingestion, discovery, AI retrieval, and downstream consumption. The engineer also establishes repeatable modeling standards and playbooks for scaling knowledge-management data products.

Design and govern data product models for unstructured/semi-structured KM content, define domain boundaries and metadata taxonomies, register and secure data products in Databricks Unity Catalog, partner with engineering and KM stakeholders, and create repeatable modeling standards to accelerate privacy/legal review and downstream AI/consumption.

Design, build, and maintain a scalable open-source data lakehouse platform (S3, Apache Iceberg, ClickHouse). Develop and optimize ETL/ELT pipelines and orchestration (Temporal), ensure platform reliability and performance, collaborate with data scientists and engineers, and implement data quality, governance, and security best practices. Focus on batch processing and cloud-native microservices environments.

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.

Build and operate the data platform behind regulatory indices: source messy public and commercial data, implement idempotent medallion ingestion pipelines, design Postgres schemas and migrations, construct auditable index methodology (normalization, weighting, sensitivity testing), and run pipelines on Prefect/ECS with strong observability and test coverage.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

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, architect, and govern scalable data platforms (data lake/warehouse). Engage stakeholders to gather requirements, implement ETL pipelines, optimize performance, and manage data ingestion using Hadoop/Spark/Kafka/Airflow and AWS tools. Support data modeling, security, and workflow orchestration.

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.

As a Data Engineer, you'll maintain and enhance a data ingestion pipeline, collaborate with data scientists and engineers, and implement features for efficiency and scalability.

Lead the design and implementation of robust data pipelines and ecosystems. Ensure scalability and reliability while mentoring team members in best practices.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Data Engineer will implement ETL/ELT solutions, design data pipelines, ensure data security, and optimize data processing systems. Responsibilities include data transformation and compliance collaboration.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

Design and migrate ETL into orchestrated Airflow DAGs within a Snowflake medallion architecture. Harden pipelines with automated testing, alerting, and failure recovery. Audit and optimize dbt for performance and cost, maintain Paradime-Airflow integrations, and ensure zero downtime for downstream BI while supporting future AI semantic layers.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Partner with internal data teams to model, clean, and transform customer, sales, and behavioral data in Snowflake for a Customer Data Platform. Build semantic views and AI-ready data models, write production-grade SQL and Python for ETL, parse nested JSON payloads, maintain documentation and git workflows, and collaborate with stakeholders to translate business needs into technical data solutions.

Design, build, and deploy scalable data lakehouse and warehouse platforms; implement and optimize ETL/ELT pipelines (Databricks, dbt); develop data models and Medallion architecture layers; manage CI/CD and Infrastructure as Code (Azure DevOps, Terraform, CloudFormation); use cloud-native serverless offerings across major clouds; ensure data security and governance; integrate pipelines with BI tools like Tableau, Looker, and Power BI; communicate technical designs to stakeholders.

Build and maintain ETL/ELT pipelines, model GTM data assets, optimize complex SQL queries, ensure data quality and governance across Snowflake/GCP, and collaborate with Data Engineers and PMs to support Salesforce-integrated revenue analytics and lead routing.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

Build and own end-to-end analytics data infrastructure: design and maintain ETL/ELT pipelines, model data in dbt, govern semantic layer and metrics, implement testing and observability, partner with stakeholders for requirements, run analyses and A/B test evaluation, and leverage AI tools to accelerate engineering and analytics workflows.

Design, automate, and maintain end-to-end data ingestion pipelines; integrate pipelines with ServiceNow and downstream systems; use Snowflake and OpenTelemetry for performance and observability; implement data validation and troubleshoot pipeline failures.

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.