Top Data Engineer Jobs in Portugal

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Design, build, and operate scalable batch and near-real-time data pipelines on AWS. Lead complex data engineering projects, translate business needs into data models and technical solutions, ensure data quality and reliability, and shape platform roadmaps. Integrate AI workflows such as LLM orchestration, embeddings, and vector search while collaborating with product and engineering teams across financial and analytical products.

Build and manage high-volume analytics and production ETL/ELT systems, ensuring reliable data ingestion, quality, observability, security, compliance, and scalable performance. Partner with Data Science, Engineering, and Product teams to support accurate Search data logging and usage. Define data-system SLAs, optimize cost and performance, and support massive datasets, experimentation, and machine-learning applications.

Develop and own highly reliable, scalable enterprise data platforms and petabyte-scale data pipelines. Design functionality for ingesting, processing, storing, and querying data; collaborate on architecture, design, and code reviews; prototype solutions; optimize big-data systems; and solve complex business problems using first-principles thinking. The role requires ownership across software engineering and product development and may involve extended hours or weekends.

Develop and own reliable, scalable enterprise data platforms operating at petabyte scale. Build end-to-end data pipelines, ingestion and processing functionality, and data products that improve business operations. Collaborate on architecture, design, code reviews, prototyping, testing, deployment, and monitoring while solving complex problems through first-principles thinking. The role requires extended hours and weekend work when needed.

Build and operate a modern data platform for network telemetry, including scalable ingestion pipelines, real-time event processing, workflow and rule engines, APIs, data models, lakehouse storage, observability, and lineage. Deploy services on Kubernetes using CI/CD, troubleshoot distributed systems, contribute to architecture and code quality, and collaborate with product, front-end, operations, and business stakeholders. Use AI-assisted development tools and participate in a light on-call rotation.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Build and improve data pipelines, integrations, and AI agents for Starlink Enterprise. Clean and enrich CRM and go-to-market data, develop MCP servers and connectors, instrument data and agent performance, and partner with sales and ops to automate workflows and reduce manual cleanup.

Improve go-to-market data quality and build integrations and AI agents for Starlink Enterprise. Implement validation, enrichment, deduplication, MCP interfaces, and automations across CRM and operational systems. Instrument data and agent performance, partner with sales and ops, and ship reusable tools and monitoring to accelerate lead-to-revenue workflows.

Design, build, and maintain scalable Hadoop-based data processing pipelines and ETL for structured and unstructured data. Implement streaming and batch solutions (Spark, Storm, Kafka), integrate multiple data sources and NoSQL stores, optimize performance, define retention, and collaborate with product and data science teams in an Agile environment.

The Lead AI Enterprise Data Engineer will architect scalable data platforms, develop AI solutions, and create real-time dashboards, while overseeing AI model deployment and integration with existing systems.

The Test Data Systems Engineer will manage data integration, build data structures, develop visualization platforms, and ensure data integrity and standardization for engineering insights.

Design, implement, and own reliable data solutions across design, manufacturing, and mission operations. Lead data architecture, migrations, ETL/replication, schema design, and optimizations. Partner with cross-functional teams, drive tradeoffs, prioritize work using data, mentor team members, and deliver production-ready data products.

Design, build, and maintain scalable cloud-native data platforms and pipelines (batch, streaming, near-real-time). Implement distributed processing (Spark), event-driven systems (Kafka), governance, security, observability, and support ML/analytics. Partner with cross-functional teams, optimize performance/cost, and mentor engineers while enabling production-grade data products.

Lead the architecture, development, and maintenance of scalable data pipelines and platform infrastructure supporting satellite operations, analytics, machine learning, and business decisions. Build data warehouses, lakehouse systems, data models, and dbt transformations; operate platforms using Databricks and related cloud services; and ensure data quality, reliability, security, governance, observability, lineage, and cost efficiency. Collaborate with product owners and multidisciplinary engineering teams on data requirements and platform capabilities.

Develop and maintain Zoox’s on-vehicle Core Data systems for capturing, storing, retrieving, and transmitting autonomous vehicle data. Responsibilities include contributing to architecture, implementing high-performance fault-tolerant systems under real-time and resource constraints, owning technical problems through rollout, and collaborating on requirements, design reviews, and code reviews.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Summer interns assist with designing and implementing IT solutions, support cybersecurity initiatives by analyzing security protocols and recommending improvements, and collaborate with data science teams to analyze data and develop actionable insights. The role provides hands-on experience, mentorship, professional development, and exposure to innovative technology projects across Honeywell Technologies.

Designs and maintains data models, databases, dictionaries, ETL workflows, and data quality processes. Partners with data owners and reporting teams to define requirements, resolve discrepancies, establish stewardship, and improve data sharing. Develops scalable PySpark and Python pipelines, optimizes SQL and Spark jobs, and implements AI/ML solutions using technologies including Snowflake and Oracle. Requires collaboration across teams, strong documentation, and adherence to data standards.

Summer 2027 intern supporting IT solution design, cybersecurity protocol analysis, and data science initiatives. The role involves collaborating with technical teams to improve security, analyze data, develop actionable insights, and enhance operational efficiency. Candidates will gain hands-on experience, mentorship, and exposure to innovative technologies while working with multifunctional teams across Honeywell Technologies.

Summer 2027 interns will assist with designing and implementing IT solutions, support cybersecurity initiatives by analyzing protocols and recommending improvements, and collaborate with data science teams to generate actionable insights. The role offers hands-on experience, mentorship, professional development, and relocation support in a dynamic, cross-functional environment.

Summer interns will assist with designing and implementing IT solutions, support cybersecurity initiatives by analyzing security protocols and recommending improvements, and collaborate with data science teams to analyze data and develop actionable insights. The role offers hands-on experience, mentorship, professional development, relocation benefits, and short-term housing for eligible interns.

Build and deploy production Data 360 implementations in enterprise customer environments. Develop ingestion pipelines, data models, identity resolution, retrieval systems, vector search, and AI-agent integrations. Write SQL and application code, test at scale, troubleshoot data and API issues, enforce security, and support production handoffs. Partner directly with customers, architects, developers, and implementation teams while documenting reusable scripts, tests, metadata, and runbooks.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Designs and owns production data schemas and models, builds AWS data infrastructure and artifact storage, and develops edge-to-cloud pipelines for robotic systems. The role also delivers ML infrastructure for training pipelines, dataset versioning, and model deployment. Candidates must have at least two years of AWS data infrastructure experience, strong distributed-systems modeling skills, ML infrastructure experience, and the ability to work across the stack. Travel to customer sites is required. Robotics, autonomy, industrial IoT, edge computing, and manufacturing experience are preferred.