Top Data Engineer Jobs in Portugal

Engineer large-scale scientific data services to enable AI for science: design data ingestion, indexing, and search for terabyte-to-petabyte datasets; expose data via high-performance APIs and usable web apps; deploy and optimize services in Kubernetes and HPC environments; collaborate with scientists and partners on data structuring, performance profiling, and secure multi-user access.

Design, implement, and maintain scalable distributed systems and storage for petabyte-scale 3D geospatial data. Build high-performance APIs, data ingestion and processing pipelines, monitoring/operational tooling, and collaborate with cross-functional teams to define requirements and drive the backend roadmap.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

The Senior Software Engineer will develop a real-time ML data platform for maritime autonomy, focusing on data processing, pipeline building, and ensuring dataset quality and reproducibility.

Build and operate cloud data platform components for ingestion, transformation, validation, and serving across batch and near-real-time pipelines. Own lakehouse and warehouse foundations, data contracts, lineage, quality checks, and performance/cost optimizations. Create orchestration patterns, shared libraries, CI checks, and runbooks. Partner with product, telemetry, analytics, and ML teams to standardize dataset publishing and ensure dependable, discoverable, and performant data for operational decisions and analytics.

Design, build, and maintain ETL/ELT pipelines and integrations (APIs, file systems, RPA). Optimize Postgres and SQL for scale, implement monitoring, lead data projects, mentor engineers, and collaborate cross-functionally to ensure reliable data delivery.

Design, build, and govern a governed Azure-centric data warehouse unifying ticketing, loyalty, and F&B data. Own identity resolution, pipeline development (Python, SQL, dbt, Airflow), environment governance, architectural audits, and mentor engineers while partnering with business stakeholders to deliver trusted data products.

Design and own a multi-tenant data transformation platform: tenant onboarding, schema/ontology evolution, correctness guarantees, RBAC/isolation, agentic and API access, real-time sync, orchestration migration, and governance to scale data products for AI-driven marketing.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead design and delivery of enterprise-scale data lineage and metadata platforms. Build cloud-native, event-driven backend services and streaming pipelines using Java/Python on GCP. Partner with architecture, governance, and product teams to improve metadata ingestion, lineage discovery, automation (including GenAI), observability, and operational reliability while mentoring engineers and championing CI/CD and secure practices.

Build and maintain scalable data processing pipelines and the data warehouse, write tested performant code, develop product features, establish data engineering standards, and collaborate across product and engineering to improve data access and quality.

The role involves designing and building scalable, reliable database systems, collaborating with product teams, and maintaining system operational excellence. Key responsibilities include improving performance, debugging issues, defining technical strategies, and enhancing engineering productivity.

Develop and apply machine learning and deep learning models for medical image analysis; collect and preprocess large datasets; build software tools and frameworks for automated image processing; collaborate with radiologists and engineers; design clinical evaluations; ensure regulatory and quality compliance for imaging algorithms.

The Cyber Data Analyst Engineer III role involves designing and maintaining data collection pipelines, troubleshooting data flow issues, and ensuring product uptime, alongside training and documentation tasks for cybersecurity efforts.

Design and build scalable data ingestion pipelines, analytics systems, and microservices to power analytics, recommendations, and personalization. Collaborate with product, engineering, and business teams to deliver reliable data infrastructure, ensure end-to-end data quality, and enable data-driven decisions across the company.

Design, implement, and operate storage and data persistence services for a Kubernetes-based hybrid cloud/on-prem platform. Build backend services, web UIs, APIs, and automation for dataset management, backup/replication, lifecycle operations, and integration with identity, auditing, and CI/CD pipelines. Participate in architecture reviews, testing, documentation, and Agile development.

The Big Data Systems Engineer will deploy and integrate the CHEETAS project across various DoD Ranges, troubleshooting and resolving deployment issues in collaboration with system administrators and software developers, while ensuring effective communication with stakeholders.

Design, build, and operate scalable batch and real-time data pipelines and infrastructure. Own end-to-end data products, establish observability and data quality, troubleshoot production issues, document architectures and runbooks, and partner with analytics, ML, and product teams to deliver reliable, self-serve data solutions.

Design, build, and optimize enterprise-scale data platforms and ETL/ELT pipelines within Azure and Microsoft Fabric. Architect ingestion, transformation, and serving layers, implement Data Vault 2.0 and medallion architectures, enable low-latency distributed processing, ensure data quality for high-volume transactional sources, and collaborate with architects, analysts, and stakeholders to deliver scalable analytics solutions.

Design and evaluate autonomy, sensor deployment, and spatio-temporal data fusion frameworks across sea, ground, air, and space domains. Develop data architectures and pipelines, integrate machine learning and computer vision algorithms, quantify uncertainty, and collaborate with analysts and UxV operators from research through production.

Design, build, and operate scalable data pipelines and systems; own data architecture, data quality, SLAs, query performance, and reusable tooling; collaborate with engineering, AI, data science, and product teams; mentor engineers and promote observability and testing best practices.

Design, build, and maintain scalable cloud data pipelines and warehousing/lake solutions. Advise customers on best practices, lead technical design, mentor engineers, and collaborate with stakeholders to deliver supportable data engineering solutions across cloud platforms.

Design, implement, and maintain cloud-based data pipelines and platforms. Advise clients on best practices, partner with stakeholders to translate requirements, provide technical leadership and mentoring, and stay current with cloud data technologies and architectures.

Lead design and delivery of cloud data pipelines and architectures, advise clients on best practices, implement ETL/orchestration, work with product owners to meet requirements, provide technical leadership and mentor engineers, and maintain scalable data warehouse and lake solutions across cloud platforms.

Lead design and implementation of cloud-based data pipelines and distributed data systems. Advise clients on best practices, partner with stakeholders to translate requirements, provide technical leadership and mentorship, and maintain scalable ETL, warehousing, and streaming solutions across cloud platforms.