Top Data Engineer Jobs in Portugal

Design and implement scalable backend services and distributed data systems. Build and maintain ETL pipelines and data warehousing, enforce data contracts, and manage cloud infrastructure (primarily GCP). Lead system architecture, mentor engineers, own operational on-call responsibilities, and collaborate with product, bioinformatics, and ML teams to enable secure, compliant data and compute workflows for research and clinical use.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Design, build, and maintain scalable self-service data and ML platform tools and workflows. Mentor junior engineers, collaborate with cross-functional teams, implement monitoring/alerts, drive infrastructure improvements, integrate new technologies, and produce technical documentation to support reliable, scalable data products and pipelines.

Design, build, and maintain scalable data pipelines and platforms. Implement ETL/ELT workflows, parse and process structured/unstructured data, and support data platform scaling, monitoring, and operations while working on cross-functional agile teams.

The Data Engineer will focus on querying Databricks tables with SQL and Python, building and optimizing data pipelines for improved data reliability and quality, and collaborating with teams to enhance data management processes.

As a Data Engineer, you will design and implement data solutions, automate processes, and support data analytics for the Department of Defense.

The Data Engineer will develop scalable data pipelines, build applications using Palantir Foundry, and collaborate with stakeholders on data integration for decision-making.

Lead end-to-end data integrations with utility customers: scope requirements, map and transform datasets, run and monitor pipelines, triage issues, and productize repeatable tooling to speed implementations.

Design, develop, and maintain batch and streaming data pipelines and ETL for large unstructured datasets. Collaborate with developers, data scientists, and analysts to build data management systems, containerize and deploy services (Docker/Kubernetes), and implement CI/CD, DevOps, and monitoring on Linux/AWS infrastructure for intelligence community missions.

Design, build, and maintain Data Vault 2.0 data warehouse and Power BI solutions. Develop ETL pipelines, data models, reusable datasets, and data governance. Perform profiling, cleansing, migrations, and collaborate with infrastructure teams to optimize data delivery, automation, and reporting.

Own and maintain the companys data infrastructure and pipelines. Design, build, and monitor ETL/ELT workflows and data architecture; integrate SaaS sources; enforce data quality, standards, and governance; partner with stakeholders to deliver analytics-ready data and meet data availability SLAs.

Design, build, and optimize enterprise-scale data models, pipelines, and cloud-native architectures to support analytics, AI/ML, and reporting. Lead data governance, metadata management, lineage, access controls, and data quality. Partner with stakeholders to translate business needs into scalable, secure, and high-performance data infrastructure, and evaluate emerging technologies to evolve the data platform.

Design, build, and maintain scalable data pipelines and platforms using Python, PySpark, SQL, and Databricks. Integrate and validate data from multiple sources (APIs, structured/semi-structured) to support analytics, operational planning, and decision-making. Lead cross-disciplinary teams, ensure data quality and performance, and communicate technical findings to stakeholders on mission-focused DoD projects.

Design, build, test, and operationalize data pipelines and solutions on cloud and Databricks. Ensure data quality, governance, security, and support incident resolution. Collaborate cross-functionally to deliver analytics, ML/AI enablement, and educate users on data products.

Develop and maintain ETL/data pipelines and perform complex data analysis to support AI/ML development across multiple classification levels. Document workflows, coordinate data requirements with stakeholders, optimize and automate data delivery, and troubleshoot technical challenges.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Design, develop, and maintain scalable ETL/ELT pipelines, Delta Lake and cloud data warehouse solutions on AWS. Build and optimize SQL and Spark workloads, develop ML/AI models (including LLM/RAG), APIs and lightweight web apps, and create dashboards and visualizations to deliver actionable insights to stakeholders.

Design, build and quality-assure data pipelines, semantic models and analytics solutions on a cloud data platform. Work in Agile squads delivering end-to-end data products, coach peers, embed test automation, and contribute to service documentation and data modelling.

Apply systems engineering and data analysis to construct reliable data products, analytic models, and interactive dashboards (Qlik, Streamlit) using DoD analytics platforms (Advana). Curate and standardize force management data, translate operational needs into requirements, support lifecycle planning, integration, documentation, and stakeholder training for DoD decision support.

Design, build, and optimize security-focused big data pipelines and architectures (Data Fabric, Datalake, Datawarehouse). Lead end-to-end implementations, automate processes, support stakeholders, and enable data delivery for analytics and security use cases.

Lead Data Security Posture Management (DSPM) across endpoint, cloud, SaaS and collaboration environments. Improve visibility into sensitive data, reduce exposure and exfiltration risk, develop DSPM/DLP workflows and monitoring, partner with engineering and architecture teams to remediate risks, and leverage AI tools like Copilot to automate operations and strengthen data protection.

Set up test, preproduction and developer environments on physical servers; act as SQL Server DBA including security auditing; develop Big Data solutions and programs; perform user acceptance and integration testing. Requires experience with DTS/SSIS/SSRS, T-SQL, C#, PowerShell, Hive/Hadoop, and Microsoft SQL Server 2000 certification.

The Data Engineer will construct data infrastructure, develop machine learning workflows, maintain data pipelines, integrate software components, and collaborate across teams to enable data-driven decisions.

The Data Engineer will design and maintain data pipelines on Azure, manage ClickHouse for analytics, and ensure healthcare compliance while supporting ML integration.

Build and own the companys core data foundation: design data models, pipelines, and trusted metrics connecting product usage, accounts, billing, and revenue. Partner with Product, Engineering, and GTM teams to improve data quality, create self-serve reporting, and automate workflows using AI. Balance foundational architecture with urgent business needs and enable consistent definitions and visibility across Sales, RevOps, Marketing, Finance, and Product.