Top Data Engineer Jobs in Portugal

As a Software Engineer 1, you will contribute to data collection, build ETL jobs, and develop a Big Data framework as part of a Scrum team.

The Data & Control Systems Engineer designs and maintains control infrastructure for launch operations, develops automation systems, and supports data acquisition efforts for the Starship Launch Vehicle.

As a Staff Data Engineer, you will lead in designing, developing, and testing software applications, ensuring quality, scalability, and reliability, while mentoring junior staff and collaborating with teams.

Lead the unification of large heterogeneous datasets for generative audio models by creating robust systems for data ingestion, deduplication, and reconciliation. Collaborate with ML researchers and develop scalable entity-resolution solutions while tracking data quality metrics.

As a Senior Data Engineer, you will develop data pipelines, architect innovative data products, and integrate AI/ML models to enhance business insights.

The Data Engineer will optimize data infrastructure, modernize SQL processes, support data governance, and mentor team members while enhancing analytics capabilities.

The Data Analyst will analyze complex datasets, lead data integration projects, evaluate power generation systems, and develop reports using SQL and visualization tools.

The Senior Associate will build and extend the data platform and AI applications, focusing on data engineering, cloud infrastructure, and AI development.

The Senior Software Engineer will develop and maintain big data applications, focusing on distributed computing, automation, and collaboration with teams to enhance healthcare solutions.

The Senior Software Engineer will architect scalable solutions, enhance self-service platforms, and tackle complex data challenges while collaborating across teams, ensuring high throughput in distributed systems.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

The role involves developing AI/ML algorithms, building ETL pipelines, optimizing data delivery, and collaborating with a multidisciplinary team. Requires significant expertise in data handling and machine learning techniques.

The role involves leading complex initiatives in data science and machine learning, architecting solutions, mentoring staff, and presenting findings to executives.

Lead the development of scalable big data solutions, focusing on batch processing and API development while ensuring security and compliance. Collaborate with teams to modernize applications and enhance performance.

The Senior Software Engineer - Data Engineer will design, build, and operate scalable ETL/ELT pipelines, manage data quality, and collaborate with teams to provide robust enterprise data solutions.

Design and build automated data validation and monitoring systems across clinical and AI pipelines. Translate business needs into validation frameworks, implement Python/C#/SQL checks, perform root-cause analysis, and embed data quality and compliance (HIPAA/SOC2) into data lifecycles. Collaborate with stakeholders to prioritize, deploy, and document automation and AI-assisted tooling.

The Lead Data Engineer will manage U.S. Data Strategy capabilities, translating business objectives into cloud data solutions, while ensuring scalability and security. This includes ETL, analytics, and technical leadership in a hybrid work environment.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.

Lead design and delivery of a cloud-based data platform: implement CDC/streaming ingestion, modeling, transformations, cross-region DB replication, observability, governance (PII, lineage, access), and enable downstream marts while mentoring engineers and driving IaC/automation-first practices.

Design, build, and operationalize scalable data pipelines and transformations on cloud platforms. Ensure data governance, quality, and security; collaborate with data science to support ML model operationalization; mentor engineers and drive architectural decisions for enterprise analytics.

Design, build, deploy, and operate scalable production data pipelines and workflows (Airflow, BigQuery, dbt). Own medium-sized platform initiatives end-to-end, ensure SLA-driven freshness, quality, and cost controls, implement monitoring and CI/CD, participate in on-call incident response, optimize warehouse performance, and collaborate with analytics, data science, product, and engineering stakeholders.

Build and productionize large-scale data pipelines and platforms to generate ML and evaluation datasets from driving logs and simulation data. Design tools for data mining, exploration, and analysis, and collaborate with Data Science and Quantitative Analytics to deliver high-impact, production-ready infrastructure and dataset generation.

Lead extraction, mapping, cleaning, transforming, and importing complex donor and fundraising data from legacy systems into Virtuous. Use SQL and dbt to design ETL transformations, collaborate on migration strategy, work with customers and internal teams, and solve complex data structure and relationship issues to align legacy data with Virtuous CRM best practices.

Design, build, and maintain data pipelines and Generative/Agentic AI solutions. Integrate LLM APIs and agent frameworks, ensure data quality, automate workflows, run proofs-of-concept, monitor systems to meet SLAs, and present insights to senior leaders. Collaborate across functions and enforce compliance and governance for GenAI outputs.