Top Data Engineer Jobs in Portugal

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

As a Data Engineer at SpaceX, you'll develop metrics and tools to assess the scaling and efficiency of the Starlink network, collaborating with various teams.

The Data Engineer designs data architectures and pipelines, supports analytics teams, solves complex data challenges, evaluates tools, and mentors team members.

The Senior Geospatial Data Engineer will design and implement cloud-based geospatial data infrastructures, optimize data processes, and lead project collaborations within the team.

The Principal Software/Data Engineer will develop and enhance software systems, ensuring adherence to design requirements, and provide input on system designs, with significant experience in software engineering and clearances required.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

The role involves migrating on-premises databases to AWS, developing Python scripts for data processing, and building AWS data pipelines. Responsibilities include supporting application teams, data modeling, and ensuring system efficiency.

The Data Engineer II will design and implement scalable data pipelines, collaborate with stakeholders, ensure data quality, and support analytics by utilizing cloud platforms and big data technologies.

The Senior Data Engineer will design, develop, and maintain robust data pipelines, collaborate with teams, and enhance data architecture.

Design, build, and optimize data pipelines, ETL processes, and data warehouse solutions; develop and support data visualizations and dashboards; enforce data governance and security; assist with data architecture, streaming, orchestration, monitoring, and cloud cost optimization while collaborating with stakeholders to deliver actionable insights.

Design, build, and maintain scalable ETL/ELT data pipelines and data warehouses using Databricks, Spark, SQL Server, SSIS, and PL/SQL. Integrate healthcare data, optimize queries and performance, monitor data quality, support HIPAA compliance and data governance, and collaborate with stakeholders to deliver analytics-ready data assets.

The role involves designing and developing AI and data solutions, collaborating with stakeholders, and operationalizing machine learning models while ensuring data governance and compliance.

Design, build, and support cloud data solutions on Azure: develop ETL pipelines, transformations (SQL/Python/PySpark), data models, storage patterns, quality checks, monitoring, and CI/CD; collaborate with stakeholders and document architecture and runbooks.

Design, build, and automate scalable data platforms (lakehouses/warehouses). Implement and optimize ETL/ELT pipelines using Databricks and dbt, apply Medallion Architecture, manage IaC/CI-CD, ensure data security and governance, and integrate pipelines with BI tools while engaging with stakeholders.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark, ADF, Fabric), enforce data governance and quality, enable Power BI reporting, lead cloud migration and CI/CD, and provide production support and performance tuning across enterprise data systems.

Support a Department of State implementation by assessing data policies and architecture, developing data pipelines, cleaning and integrating datasets (using Palantir Foundry), and producing analyses and visualizations. Create governance documentation, catalog data fidelity issues, coordinate with non-technical users, and recommend improvements to meet analytical and mission goals.

Build and maintain Databricks data pipelines ingesting Snowflake and data lake sources, implement medallion architecture transformations, ensure pipeline health and monitoring, create curated datasets for analytics and ML, and partner with data scientists and stakeholders.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

Lead design, build, troubleshoot, and deliver enterprise-scale data solutions on Azure and Hadoop. Provide technical oversight, mentoring, enforce standards, code, unit test, and collaborate with cross-functional partners to implement scalable data pipelines and analytics platforms.

Design, build, test, and operationalize data pipelines and solutions using Databricks and SQL. Ensure data quality, governance, and stakeholder collaboration to enable BI and AI/ML analytics; troubleshoot, document, and support data products.

Design, build, and support enterprise data integrations, pipelines, and analytics-ready data products. Extract, transform, validate, and deliver data securely and reliably using cloud analytics platforms, CDC tools, API integrations, and BI artifacts to support operational reporting and decision-making.

Design, develop, and maintain scalable data pipelines, stores, and ETL/ELT workflows. Parse and process structured and unstructured data, deploy and operate data platforms, and collaborate with cross-functional teams to support analytics and reporting. Requires Secret clearance and a bachelor’s degree.

Design, build, and maintain cloud-based ELT data pipelines and analytics-ready datasets using Snowflake, dbt, and integration tools (Fivetran/Airbyte). Implement CDC and incremental loading, optimize Snowflake performance, enforce data quality and monitoring, create dimensional models for reporting, enable CI/CD for dbt projects, and collaborate with analytics and stakeholder teams to support reporting and advanced analytics.

This role involves ensuring the reliable operation of optical transport systems in data centers, coordinating with clients, and performing technical audits and installations while leading a team.

Develop and maintain backend services for a data platform, ensuring reliability and performance. Collaborate on scalable data models and contribute to architecture decisions.