Top Data Engineer Jobs in Portugal

Lead design and build scalable, secure data platforms and pipelines for AI/ML. Own ingestion, transformation, storage, quality, monitoring, and real-time/batch processing. Collaborate with data scientists and ML engineers, drive data modeling and MLOps best practices, ensure governance and mentor junior engineers.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

As a Data Platform Engineer/Platform Lead, you'll design, support, and optimize data platforms for digital products while collaborating with cross-functional teams and managing complex DevOps initiatives.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

Technical lead for Finance Technology data engineering: design and build scalable batch and real-time data pipelines (Hadoop/Spark/PySpark/Scala/Java), optimize data models and APIs, drive cloud-based data architecture (AWS/GCP/Azure), enforce governance/security, and mentor engineers to deliver enterprise financial data services.

Lead development of a long-term enterprise data quality strategy for cloud environments. Build data inventories, implement profiling scripts, anomaly detection, field validation rules, lineage checks, and completeness scoring. Develop governance policies, stewardship standards, and access controls while coordinating with platform engineers, cybersecurity, and mission teams to maintain metadata and catalog traceability.

Lead design and delivery of scalable Data and AI solutions, mentor engineering teams, drive GenAI proof-of-concepts, align technical strategy with business goals, and influence cross-functional stakeholders.

Senior Data Engineer responsible for designing and implementing data solutions, ensuring data quality, and optimizing data systems.

The Staff Software Engineer will lead the evolution of Slickdeals' data engineering ecosystem by architecting scalable platforms, maintaining data workflows, and mentoring engineers in a collaborative environment.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

Design, build, and operate scalable, secure data infrastructure including access control, financial reporting pipelines, cloud storage reliability, and data platform tooling. Collaborate with data scientists, analysts, and stakeholders to ensure data integrity, performance, cost-efficiency, compliance, and rapid recovery at petabyte scale.

Lead architecture and build control plane services for a petabyte-scale compute platform. Design multi-tenant resource management, intelligent scheduling, GPU and AI/ML workload orchestration, and Kubernetes operators. Drive integration with infrastructure teams, lead code reviews, set standards, and mentor engineers to deliver production-grade distributed systems.

As a Research Engineer, you conduct pioneering research in autonomous systems and develop models using large-scale data and advanced simulators, collaborating with scientists and contributing to high-quality publications.

Lead airworthiness data strategy including analytics, visualization, digital standards, and integration into the Digital Engineering Ecosystem. Drive policy and standards development, data governance, taxonomy/metadata improvements, training, and cross-functional coordination to support airworthiness certification and modernization.

The Technical Program Analyst will develop and automate applications using Microsoft Power Platform, focusing on ETL workflows and stakeholder collaboration for technical solutions.

Lead and grow the data engineering team to evolve a cloud-native data platform: design pipelines, improve ETL/ELT and orchestration, ensure data quality and governance, enable BI and data products, manage infrastructure budget, and enforce security and operational best practices while partnering with product and business teams.

Early-career Data & AI Engineer supporting client work and applied R&D. Prepare, clean, and validate heterogeneous data; build reproducible Python/SQL workflows; support ML and generative-AI tasks (embeddings, RAG, evaluation); prototype tools; document assumptions; participate in code review and translate project lessons into reusable assets.

Design, operate, and scale end-to-end data infrastructure and pipelines. Build reusable data models and a semantic layer, ensure data quality and privacy, implement observability and SLAs, support marketing and growth feeds, partner with cross-functional stakeholders, and enable ML/AI consumption and reverse ETL workflows.

Design and build scalable platforms for data ingestion and storage, collaborate with hardware engineers, and define engineering processes at Sift.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design and implement SQL transformations and datasets, manage data models in Databricks, optimize performance, and ensure compliance with data governance standards.