Top Data Engineer Jobs in Portugal

Seeking a Sr. Big Data Engineer with 8 years of Java/Scala experience, Unix/Linux systems knowledge, and expertise in big data technologies and cloud deployment.

The Senior Data Engineer will optimize data architecture, troubleshoot integrations, and refactor legacy systems within a product team focused on client applications.

Develop reliable data pipelines and applications, collaborate on design and architecture, solve business problems, and own software development.

Develop data analysis systems, applications, and pipelines for SpaceX operations. Collaborate on architecture and code reviews, own software development.

The role involves full application life cycle participation, system maintenance, stakeholder interaction, and integration development, focusing on data integration and middleware solutions.

As a Technical Solutions Engineer, you will design and implement power delivery solutions for data centers, ensuring compliance and collaborating with teams for optimization and technical guidance.

The Senior Data Engineer develops software and data pipelines, ensures software quality, collaborates with teams, and maintains system documentation.

As a Senior Software Engineer at Snowflake, you will design and build data ingestion and processing pipelines, develop distributed systems, and collaborate on software reliability and performance across various cloud platforms.

Lead the design and optimization of scalable data ingestion pipelines, develop reusable ETL components, and mentor junior engineers while leveraging expertise in Python and AWS for the Arine platform.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

Design, build, and operate large-scale AWS-based ETL pipelines using Glue/PySpark to ingest, transform, and curate multi-format data into Apache Iceberg tables. Ensure data quality, metadata management, semantic layer creation (Trino/Athena), CloudFormation deployments, documentation, and compliance within a FedRAMP federal agile environment.

Own and improve the company data stack: expand the data warehouse, build and streamline Python-based ingestion and ETL processes, refactor dbt models, maintain data quality, standardize reporting, and collaborate with engineering and analysts. Act as technical owner for parts of the stack and proactively drive improvements and automation.

Design, build, and maintain ETL/ELT data pipelines and data models from SQL Server, PostgreSQL, SaaS, and internal apps. Ensure data quality, monitor ingestion, optimize Snowflake performance, collaborate with stakeholders to deliver dashboards and analytics, and recommend data architecture improvements.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.

Design and build core access control and data governance systems across a multi-tenant platform. Implement fine-grained permissions, auditability, policy enforcement, metadata management, and compliance features. Collaborate with product managers, own large components end-to-end, and mentor engineers while balancing security and usability.

Serve as a data quality and QA engineer on a federal data platform: build automated AWS Glue/Deequ data quality checks, validate schemas and ETL pipelines, create and run unit/integration tests with 90% coverage, support IV&V and UAT, perform static/security scans, produce test documentation, monitor production ETL, and collaborate with data stewards and IV&V teams.

Own and build the end-to-end data layer: design high-throughput real-time ingestion, architect petabyte-scale open table formats, implement and optimize Spark-based batch/stream pipelines, enable Kafka/Flink integrations, ensure performance/reliability/cost efficiency, define data contracts and schemas, and establish infrastructure best practices on AWS GovCloud, Kubernetes, and Airflow.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Design, build, and maintain data ingestion, ETL, storage, and analytics systems to measure and scale Starlink network performance (including Wi‑Fi). Automate metric aggregation, detect widespread application issues, lead investigations, fuse multi-source data, and apply analytics and ML models to vehicle- and ground-generated data.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Design, build, and maintain near real-time manufacturing dashboards and decision-support tools. Translate plant needs into intuitive visualizations, create reusable components, ensure data reliability and performance, collaborate with OT/IT and operations, and provide documentation, training, and governance-aligned deployment.

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Design, build, and maintain interactive Power BI dashboards (including Fabric workspaces). Analyze data to identify trends, ensure data quality, collaborate with stakeholders to gather requirements, and present actionable insights to support decision-making.

Lead data engineering projects across cross-functional teams. Design resilient batch and real-time analytics systems, optimize pipelines and infrastructure, integrate ML models, and provide technical leadership, coaching, and mentoring to improve reliability, scalability, cost-efficiency, and maintainability.