Top Data Engineer Jobs in Portugal

Design, build, and operate large-scale data acquisition pipelines and backend services. Implement ETL/ELT for batch and streaming flows, develop Java production services, ensure schema evolution and data quality, optimize performance and cost, and provide observability. Collaborate with product, data science, and platform teams to translate requirements into production-ready systems.

Design, build, and optimize scalable end-to-end data pipelines and Snowflake data warehouses. Develop BI solutions (Power BI/Tableau/Cognos), enforce data quality/governance, integrate investment data vendors, collaborate with stakeholders, mentor junior engineers, and follow SDLC/DevOps best practices.

The Data Engineer designs and maintains scalable data products, implements quality checks, and supports analytics and reporting needs, collaborating closely with business stakeholders to ensure data reliability.

As a Forward Deployed Data Engineer, you will build data management platforms for maritime awareness, implementing data pipelines, models, and improving workflow efficiency. You'll work on-site, writing code and providing technical solutions, ensuring high-performance results.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

As a Data Engineer, you will design financial knowledge graphs, create graph processing pipelines, and implement best practices for knowledge graph development.

Build and own customer data ingestion and large-scale pipelines powering AI workers. Design syncing, retrieval, and data lake systems, improve reliability/observability, partner with product/backend teams, and contribute across infrastructure to give AI agents access to enriched customer data.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Design, build, and maintain scalable AWS-based data pipelines, data lakes and warehouses. Implement data modeling and warehousing for Redshift/Athena, ensure data quality, optimize queries, and develop production-grade Amazon QuickSight dashboards with security (RLS), performance tuning, and stakeholder-driven reporting.

Design, build, and operate high-performance inference platforms for serving large ML models. Focus on request routing, batching, caching, autoscaling, GPU utilization, observability, performance engineering, and production reliability for diverse model workloads.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support enterprise analytics, ML, and reporting. Ensure reliable, performant production data platforms and troubleshoot, document, and optimize workflows.

Design, build, and operate production LLM-driven agents that discover and verify business web presence, link people to entities, score legitimacy/risk, and maintain shared agent infrastructure (tool registry, eval harness, instrumentation). Own model selection, prompt/tool engineering, eval methodology, and cost/latency optimization for enrichment pipelines.

Design, build, and operate reliable, scalable backend services and platform capabilities for monetization (pricing, billing, ads, payments). Own architecture, integrations, correctness, observability, and operational excellence. Lead cross-functional technical projects, write RFCs, mentor engineers, and deliver reusable developer tools and data products for product, finance, and GTM teams.

Build, test, and deploy scalable data platform services supporting ETL/ELT, streaming, and analytics. Collaborate with global teams, troubleshoot production issues (on-call), optimize performance, and maintain documentation and observability for platform reliability.

Design, build, test, and deploy scalable data platform services (ETL/ELT, streaming, batch) using cloud and distributed processing frameworks. Support production on-call, collaborate with global teams, improve performance, and maintain platform documentation and observability.

Lead design, development, and administration of enterprise Power BI reporting, semantic models, and Azure-based data pipelines. Deliver executive dashboards, KPIs, advanced analytics, and governance; mentor teams and modernize BI capabilities while ensuring performance, security, and data quality across the organization.

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

As a Data Engineer II, you will develop and manage data lakes and warehouses, monitor database performance, optimize queries, and automate tasks using infrastructure as code.

Design, build, test, and maintain production-ready, scalable API services and platform solutions using microservices and containerization. Collaborate with engineers, Data Engineers, CloudOps and Product to deploy and operate services on cloud platforms, ensure observability and readiness, and participate in Agile lifecycle activities.

Partner with business leaders to define data problems, process and analyze complex datasets, build predictive models and ML algorithms, perform EDA and A/B testing, create dashboards and visualizations, and work with data engineers to build reliable data pipelines to inform strategy.