Top Data Engineer Jobs in Portugal

Entry-level Data Engineer responsible for building and maintaining cloud-based data assets and pipelines, improving data quality and automation, investigating data integrity issues, and collaborating with architects, analysts, and stakeholders. Uses SQL and Python, participates in Agile practices, leverages AI-powered developer tools for spec-driven development, and follows responsible AI and data privacy practices.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

The Data Engineer III will develop and maintain data pipelines, ensure data quality, and support analytics and data science efforts. Responsibilities include collaborating with stakeholders, mentoring peers, and implementing data engineering standards.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

Design and maintain scalable data pipelines and architectures, implement ETL and data quality checks, build Tableau dashboards and Prep flows, optimize workflows, collaborate with stakeholders, and mentor junior data engineers to support analytics and reporting.

Design, build, and optimize scalable data and AI platforms on Azure. Focus on data pipelines, real-time processing, and AI/ML integration using Databricks and modern cloud technologies.

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.

The AI Automation Lead will architect and deliver multi-agent AI workflows, develop full-stack applications, and lead technical decisions across engineering teams.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Own and operate the end-to-end consumer data pipeline: ingest, clean, transform, and unify multi-source behavioral data into a deduplicated identity graph. Build and run batch and streaming pipelines, enforce data-quality and observability, support low-latency AI query access, perform root-cause incident analysis, and ensure privacy, compliance, and clear data lineage. Collaborate across product and fulfillment teams to maintain SLAs and document data contracts and architecture.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Design and maintain performance and advisor service metrics for OnStar features. Build dashboards and recurring reports using Databricks, SQL, Power BI/Tableau. Investigate end-to-end system performance, document workflows, identify gaps, and implement improvements. Collaborate with IT/operations, automate analysis tasks, and ensure SLA tracking and system requirement documentation.

The Data Engineer II will maintain CI/CD pipelines, develop AI/ML platforms, process telecom data, and optimize data storage and retrieval strategies.

Design, build and maintain scalable data pipelines and automated workflows using Python, Airflow, DBT and cloud warehouses. Partner with Analytics, Sales and Marketing to deliver curated marketing datasets, enforce data quality through validation and CI/CD, and support production deployment and monitoring of Enterprise Data Lake/ODS solutions.

The Snowflake Developer designs, develops, and maintains Snowflake environments, data models, and automated data pipelines, ensuring data quality and collaboration with cross-functional teams to meet analytical needs.

The Data Engineer III will design, implement, and optimize data services, ensuring they meet business needs and drive automation and best practices across Crunchyroll's data environments.

The Data Engineer will design and develop Java-based applications, optimize APIs, and collaborate with teams, focusing on cloud integration and SQL queries.

The Distinguished Data Engineer will lead data modernization efforts, build enterprise data platforms, mentor talent, and promote engineering excellence within Capital One.

The Data Engineer will design data models, build data warehouses and lakes, automate data pipelines, and enable data accessibility while maintaining security standards in a complex environment.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Design, build, and maintain scalable data pipelines and ETL processes. Collaborate with stakeholders to gather requirements, optimize databases and queries for analytics, manage data integrations, ensure data quality, troubleshoot workflows, participate in code reviews, and mentor junior engineers while applying modern data technologies and cloud services.

Design and build high-volume, low-latency data solutions: ingest and normalize diverse data sources, implement ETL and streaming pipelines, build CI/CD for data software, and maintain observability and monitoring.

The Data Engineer II supports data infrastructure operations, resolves issues, ensures platform stability, and implements improvements while collaborating with engineering and analytics teams.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.