Top Data Engineer Jobs in Portugal

Design, build, and optimize scalable data infrastructure and pipelines on AWS using Spark/Scala. Store and model data in appropriate engines and formats, implement knowledge graphs accessible via batch jobs and APIs, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver performant, cost-effective data services.

Design and implement scalable data infrastructure and pipelines on AWS using Spark and Scala. Build fault-tolerant batch and streaming workflows, optimize storage formats and engines, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable data assets and APIs.

Lead design and implementation of identity resolution and data governance systems. Build batch and streaming pipelines, APIs, and services to ingest, normalize, link, and version identity data. Ensure auditable matching, data lineage, quality checks, schema enforcement, access controls, and privacy-by-design. Collaborate cross-functionally with product, analytics, legal, privacy, and security teams and create documentation, monitoring, and runbooks.

Own and modernize core data infrastructure and pipelines to ensure accurate usage capture, transformation, and monetization. Contribute to architecture, maintain integrations with billing/CRM/payment platforms, evolve customer account UIs, and improve legacy Laravel systems while collaborating across a distributed team.

The Data Platform Engineer will support Corporate Data Science and Analytics teams by curating datasets, optimizing code, and building ETL flows for data products. Responsibilities include managing production services and using cloud technologies.

As a Principal Data Processing Engineer, you will enhance a data processing engine, collaborate on open-source platforms, and optimize performance.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

The Data Engineer will architect and maintain modern data platforms, manage ETL/ELT pipelines, integrate diverse data sources, and champion data governance while supporting advanced analytics and AI workloads.

The Senior Data Engineer designs, builds, and maintains data pipelines and ensures data quality controls while supporting data analytics and reporting needs.

Lead engineering strategy and hands-on implementation for Finance & Actuarial data platforms. Architect no-touch automated data pipelines, drive cloud migrations (Azure Synapse/Databricks/Snowflake), embed AI-assisted development and modern DevOps, establish CI/CD and runbooks, resolve complex incidents, evaluate tooling, and mentor engineering squads while communicating strategy to executive stakeholders.

Design, build, and operate cloud and edge data pipelines for robotic systems. Manage large multimodal datasets, implement on-device capture (MCAP/Protobuf), automate MLOps/DataOps workflows, ensure data quality, and build tools for debugging, experimentation, and production deployments across AWS and edge devices.

The Principal Software Engineer will design a semantic model architecture for data products, ensure performance, and lead technical initiatives while collaborating with teams to optimize data consumption across the platform.

The Staff Engineer - Data designs and operates scalable data systems on AWS, ensuring data integrity, consistency, and performance across various platforms. They lead the data architecture evolution, improve data observability, and mentor other engineers.

Design, build, and own data infrastructure and ETL/ELT pipelines (Airflow, S3, Lambda) into a governed Snowflake warehouse. Implement data quality, IaC (Terraform/CDK), CI/CD, and analytics solutions (SQL, Tableau). Enable ML/LLM use cases like forecasting and anomaly detection and deliver automated reporting and data-driven marketing outputs.

Develop and maintain a scalable data platform for SpaceX's Starlink program, collaborating with engineers to optimize data processing and enhance business operations.

The Senior Data Engineer will design, develop, and maintain data platforms, create ETL processes, handle data quality issues, and mentor teammates while ensuring scalability and performance in data solutions.

The role involves building and maintaining AI infrastructure, optimizing training performance, and developing tools for robot training and data workflows.

Lead design and implementation of AWS-based data lake and pipelines (batch and streaming), build ML models on SageMaker, integrate foundation models via Bedrock, implement entity resolution, support Azure-to-AWS migrations, ensure data quality/security, and collaborate with cross-functional teams to deliver analytics and ML-powered solutions.

Design and build scalable end-to-end ETL/ELT data pipelines, process log and semi-structured data with Python and dataframes, and architect scalable database schemas (partitioning/sharding). Ensure data traceability, validation, monitoring, and enable datasets for machine learning model training and deployment while collaborating with data scientists and engineering teams.

The role involves developing, deploying, and mentoring in machine learning models for bioprocess development, while collaborating with scientists and engineers.

Lead design and operation of identity resolution and data governance systems. Build batch and streaming pipelines, APIs, and tooling for identity ingestion, matching, lineage, quality, access controls, and privacy compliance (GDPR/CCPA). Collaborate with product, analytics, legal, and security to ensure privacy-by-design, monitoring, and documented runbooks.

Design and implement scalable data infrastructure in AWS using Spark and Scala. Evolve core data pipelines, store data in optimal engines/formats, build knowledge graphs and APIs, optimize AWS resources, implement automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable, large-scale data services.

Design and implement scalable AWS-based data infrastructure using Spark and Scala. Build fault-tolerant batch and streaming pipelines, optimize storage formats and engines, collaborate with Data Science and Product, implement automated data quality checks, and deliver performant data assets and APIs.

Lead architecture and strategic direction for Pinterest's data warehouse, analytics tools, and data governance. Drive cross-functional initiatives to build scalable data platforms, AI-assisted pipeline tooling, and analytics capabilities. Mentor engineers, define policies and tooling, and deliver measurable adoption and business impact across the company.

Lead design and implementation of Pinterest's exabyte-scale data lake storage and metadata systems. Drive architecture and execution for scalable storage features supporting large ML/AI workloads, collaborate across data/ML/infrastructure teams, and contribute to open-source projects (Iceberg, Spark, Flink).