Top Data Engineer Jobs in Portugal

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Lead delivery of enterprise data governance, MDM, and modern data-platform components including Snowflake, CDC pipelines, schema-first contracts (JSON Schema/Avro/Protobuf), data catalog and PII classification, CI/CD schema management, AWS data services, and day-to-day leadership of an offshore delivery team.

Lead design, configuration, and delivery of advanced automation and control projects for microgrids, data centers, and critical infrastructure. Provide control system architecture, manage project scope/schedules/budgets, oversee testing/commissioning, collaborate with global teams, and drive process improvements and innovation.

Build and optimize Ray Data: improve performance and scaling for multi-modal batch inference, implement production data-loading solutions, ensure stability and fault tolerance at high scale, and collaborate with customers to scale AI workloads.

The Sr. Data Engineer will design, implement, and optimize data solutions in Snowflake, collaborate with clients, and mentor junior engineers.

Design, build, and maintain scalable data pipelines and a customer-facing recommendation engine (including LLM logic). Manage data warehouse, data quality monitoring, CI/CD for pipelines, and partner with product, analytics, engineering, and marketing to ensure consistent metrics and observability.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.

Lead design, build, optimization, and operation of scalable Databricks-based batch and streaming data pipelines for healthcare programs. Ensure DataOps, data quality, lineage, observability, CI/CD, and compliance with CMS and federal healthcare regulations. Collaborate with architects, data scientists, and stakeholders, troubleshoot production issues, and mentor junior engineers.

Senior Staff Data Engineer designs, builds, and owns cloud-based ETL/ELT pipelines and data products for Sales and Underwriting. Responsibilities include solution architecture input, CI/CD implementation, data quality and replication, hybrid cloud integration (AWS/Azure), mentoring, and evaluating new data technologies to support analytics and operational decision-making.

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.