Top Data Engineer Jobs in Fortaleza

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.

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.

Refactor, modernize, and scale existing Python production codebases and data pipelines. Improve architecture, testing, observability, and deployment automation while producing documentation and collaborating with engineering teams to increase reliability and performance of forecasting systems.

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.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Design, build, and optimize ETL/ELT pipelines and distributed data processing (Python/PySpark) on AWS. Implement data lake architectures, partitioning, and migrations from legacy RDBMS. Monitor pipeline health, ensure data quality and governance, support observability, and collaborate with analysts, scientists, and infra teams to deliver scalable analytics solutions.

Design and operate scalable, low-latency ETL/ELT and streaming data pipelines across ClickHouse, Postgres, Athena, and cloud sources; manage schema evolution, data modeling, sharding/partitioning, CI/CD for data, monitoring, and lineage; collaborate with engineers and data scientists to productionize ML/AI models and mentor team members.

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.

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.

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

Design and maintain Azure-based data pipelines, model and integrate on-prem and cloud data, build Power BI/Tableau dashboards, write complex SQL/Kusto queries, manage cloud costs, and guide teams on data best practices within Agile teams.

Lead FinOps and data initiatives to optimize Databricks workloads, monitor cloud spend, build executive dashboards, define FinOps governance, and partner cross-functionally to drive cost-efficient architecture and measurable business outcomes.

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.

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.

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

Enable AI on a Data 4.0 platform using Snowflake Cortex: build AI-ready and semantic data products, support metric intelligence and anomaly detection, define reusable Cortex patterns, ensure governance, and collaborate with analytics and platform teams.

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.

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

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.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

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.

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.

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.

Design, build, and maintain ETL pipelines and data transformations (Microsoft Fabric, SQL, Python, Power Query). Implement data quality checks, incremental loads, dimensional models, and documentation. Troubleshoot operational issues, support UAT, and collaborate with leads on architecture and semantic models to power analytics and reporting for the Mortgage Cadence platform.

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