Top Data Engineer Jobs in Portugal

Design, build, and maintain ETL/data pipelines and data warehouse solutions. Ensure data quality, perform data modeling, support analytics and data science, act as SQL Server DBA, integrate third-party systems/APIs, optimize platform performance, collaborate with stakeholders and vendors, and manage platform roadmap and reporting backlog while following IT policies and Agile/CI/CD practices.

As a Data Engineer, you will develop data-driven decision analysis products for the DoD, ensuring effective stakeholder engagement, and leveraging advanced analytics and machine learning techniques.

The Data Engineer will develop data-driven decision analysis products, provide analytic consulting, support stakeholders with tools, and optimize defense systems for national security.

As a Palantir Data Engineer, you'll work on data exploration and analytics projects, apply innovative methodologies, and support military missions using AI solutions.

Design, build, and maintain scalable data lakes, warehouses, and production ETL/ELT pipelines. Automate data pipelines, ensure data quality via testing, collaborate with architects, data scientists, and DevOps, integrate data solutions into client environments, and implement data recovery and security measures.

The Senior Geospatial Data Engineer will design and deliver geospatial data pipelines, manage data lakes, develop analytical environments, and integrate geospatial tools.

Develop database designs and architecture in Azure, secure data migrations, and conduct data profiling while collaborating with teams on technical designs.

As a Founding AI/Data Engineer, you'll design and build AI-driven products, collaborate across teams, and ensure system reliability while enhancing user experiences.

Design, build, and operate end-to-end ELT data pipelines on Azure/Databricks. Implement DLT data quality checks, CDC, schema evolution, dimensional models for Power BI, monitoring, CI/CD, and operational runbooks while partnering with architects and BI stakeholders.

Build and maintain Python data pipelines and integrations to make enterprise data discoverable. Develop metadata enrichment, semantic knowledge layers, vector embeddings, and search/index tuning. Maintain Databricks/AWS Glue pipelines, sync taxonomies/ontologies, build APIs/MCP servers, and troubleshoot data quality and pipeline issues to support retrieval-augmented generation and AI-powered discovery.

Lead design and delivery of robust, scalable data pipelines and architectures in Azure Databricks to validate and transform policyholder data for actuarial modelling. Ensure data quality, governance, monitoring, and integration of multiple sources. Optimize performance, provide technical leadership to engineers/analysts, and transition the solution to BAU with audit-ready controls in a regulated financial services environment.

Lead design and delivery of large-scale, cloud-native services powering millions of devices. Architect solutions for scalability and integration, own full service lifecycle in Kubernetes, mentor engineers, participate in on-call support, and drive engineering best practices across cross-functional teams.

Build, operate, and optimize large-scale data pipelines and warehouse schemas. Own Airflow orchestration, AWS-based data security (Lake Formation), automated data quality, and production incident resolution. Partner with stakeholders, mentor engineers, and drive DataOps best practices for performance, cost efficiency, and governance.

Develop and optimize SQL-based data solutions and ETL/ELT pipelines using DBeaver. Troubleshoot data quality and performance, validate data prior to production, document transformations, and collaborate with application owners and business stakeholders. Support testing, deployments, and knowledge transfer in a healthcare data environment.

Lead technical strategy and implementation of V-BAT fleet data analysis pipelines and tooling. Transform raw flight, simulation, and production data into metrics, dashboards, and reports; perform fleet-wide studies, anomaly investigations, and root-cause analysis; partner with cross-functional teams and mentor engineers on analysis best practices.

Design, build, and maintain Python data pipelines and business data objects. Implement source-to-target mappings, CI/CD pipelines, unit and integration tests, and deploy solutions. Apply software design and architecture best practices, mentor team members, and resolve complex technical problems across functional teams.

Lead and mentor a data engineering team to design, build, test, and support cloud-based data solutions. Develop distributed, real-time and batch data pipelines using Java/Scala/Python, NoSQL and RDBMS, and cloud data warehousing (Redshift/Snowflake). Collaborate with product managers and Agile teams to deliver performant, production-ready systems.

Design, build, and operate end-to-end data and AI systems: scalable ETL pipelines, model development and deployment, backend services/APIs, cloud infrastructure, MLOps, data governance, and lead technical reviews while mentoring engineers and collaborating with stakeholders.

Lead and administer cloud data warehouse platforms (e.g., Snowflake), design scalable data architectures and models, build and maintain ETL/ELT pipelines, implement CI/CD and version control, optimize performance and costs, migrate/load data from APIs/SaaS/ERP, and ensure security, governance, and testing best practices across the data lifecycle.

Design, build, and operate scalable, fault-tolerant data pipelines ingesting EHR and device data (ADT, meds, notes, vitals). Transform, validate, and load into MySQL and Snowflake on AWS. Ensure HIPAA/SOC2-compliant handling of PHI, monitoring, incident response, and collaboration with ML, backend, and clinical teams to support analytics and model workflows.

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

Analyze and validate multi-modal robotics data, build automated validation pipelines and visualization tools, define quality metrics and schemas, synchronize cross-modal timestamps, catalog failure modes, and work with collection teams to trace and fix data issues.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.

Design, build, test, deploy, and monitor batch and streaming data pipelines and ETL processes. Evaluate and adopt data technologies (including Adobe Experience Platform and CDPs), collaborate with data scientists, architects, and business partners to ingest and prepare large semi-structured/unstructured datasets for ML training, model execution, and search analytics, and implement automation, documentation, and tooling for scalable analytic data systems.