Top Data Engineer Jobs in Portugal

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.

As a Lead Data Engineer, you will design and maintain data platforms, oversee data ingestion pipelines, enforce standards, collaborate on solutions, and ensure best practices in healthcare data operations.

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

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

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

The Lead Data Engineer will shape enterprise data solutions, lead a team in building data pipelines, ensure project execution meets standards, and mentor team members while focusing on scalability and performance improvements.

The Staff Software Engineer will design, build, and maintain scalable data infrastructure, ensure reliability, mentor engineers, and enhance data workflows in a collaborative environment.

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.

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.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Partner with stakeholders to define metrics and deliver dashboards and insights. Build and optimize data models in Amazon Redshift, design and maintain scalable ETL pipelines using Python and Apache Airflow, monitor data quality and pipeline performance, and participate in code reviews and engineering best practices to support analytics and reporting needs.

Lead architecture and implementation of an AWS-native data platform powering analytics, ML, and AI workflows. Build scalable ETL/ELT pipelines (Airflow, Fivetran, dbt), enforce governance, observability (Datadog), and data quality, and provide technical leadership across Data Science, Analytics, Product, and Engineering.

Design and build scalable cloud data pipelines for high-volume manufacturing and IoT data. Bridge OT/IT systems for real-time extraction using industrial protocols, implement medallion/lakehouse architectures on Databricks/Snowflake/AWS/Azure, and apply analytics and lean methodologies to improve yield, uptime, and efficiency.

Senior data scientist role focused on industrial manufacturing analytics: build scalable cloud data pipelines, develop and deploy ML models for predictive maintenance, anomaly detection, and forecasting; integrate OT/IT systems (MES/SCADA/ERP) using industrial protocols; apply statistical methods, A/B testing, and lean/Six Sigma practices to improve yield and uptime; communicate findings to operations and executives.

Design, build, and operationalize scalable data solutions and pipelines for customers. Support data ingestion, transformation, quality, governance, and analytics to enable AI/ML and BI. Troubleshoot defects, test data flows, collaborate across teams, and advise customers on technical decisions and best practices.

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.

Build and maintain scalable ETL pipelines using Python, Spark, and Airflow; collaborate with AI/ML and Product teams to deliver AI-native data products; identify and resolve ETL bottlenecks; ensure code quality through reviews and tests; own sprint deliverables and contribute to roadmap planning and major epics.

Design, build, and maintain scalable cloud data and AI solutions. Create ETL/ELT pipelines, implement data quality and modeling, use PySpark/SQL, deploy ML models, enable semantic search and RAG, expose services via APIs, contribute CI/CD, and ensure data governance, security, monitoring, and documentation while collaborating with stakeholders.

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.

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.

Build and operate LLM-augmented ETL pipelines and domain-specific information retrieval systems. Design, deploy, and optimize production-grade data ingestion, databases, and infrastructure on AWS. Own projects end-to-end, collaborate with leadership and customers, and refine data priorities based on user feedback.

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.

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.

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.

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.