Top Data Engineer Jobs in Portugal

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.

Develop and own a highly reliable, scalable enterprise data platform supporting petabyte-scale data ingestion, processing, storage, and querying. Build end-to-end data pipelines, prototype solutions, participate in architecture and code reviews, and solve complex business problems using first-principles thinking. The role requires full-stack software engineering, data engineering, and collaboration across teams to improve Starlink and SpaceX operations.

Own and evolve Fanatics’ production PostgreSQL/RDS databases and Kafka/MSK streaming platform. Responsibilities include query and schema review, performance tuning, incident response, GitOps-based topic management, CDC pipeline reliability, self-service infrastructure tooling, upgrades, capacity and cost optimization, and cross-team infrastructure standards. The role serves as a technical escalation point, partners with product engineering teams, mentors engineers, and participates in on-call rotations.

Own and scale the data platform powering ReadyOn's real-time labor OS. Design and operate batch and real-time pipelines, transformations, and data services (Python, PySpark, Airflow, Glue, Snowflake, Postgres). Build connectors, observability, data quality, and feature-store infrastructure while partnering with AI, backend, and product teams and providing technical leadership and mentorship.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, build, and maintain scalable ETL pipelines, data warehouses, and analytics infrastructure integrating EMR, revenue cycle, and operational data. Ensure data quality, HIPAA compliance, support reporting/BI, enable analytics, and collaborate with clinical, finance, and IT teams.

Lead enablement and coordination across domain teams to establish enterprise cloud data standards, platform onboarding, and data warehouse integration. Drive adoption of data engineering frameworks, reusable components, governance, data quality, stakeholder alignment, and continuous improvement to scale platform delivery across multiple teams.

Design and build scalable backend services, data systems, pipelines, cloud infrastructure, and AI-enabled features for an enterprise SaaS platform. Lead technical projects, improve reliability and security, automate engineering workflows, and collaborate with product, design, and data teams. The role requires hands-on ownership in a fast-moving startup environment, with a focus on distributed systems, architecture, performance, compliance, and operational excellence.

Designs, builds, and operates high-throughput streaming and batch data infrastructure for AI products. Responsibilities include developing Kafka, Flink, and Airflow pipelines; managing analytical data layers; optimizing query and pipeline performance; implementing Terraform and GitOps practices; improving observability, reliability, and cost efficiency; partnering with product and research teams; and participating in on-call operations and automation initiatives.

Build and operate Helios’s acquisition and document-processing infrastructure, including web crawlers, connectors, fault-tolerant pipelines, multilingual document and media processing, OCR, extraction, enrichment, provenance, replay, and backfills. Scale secure CPU/GPU inference systems across cloud and restricted environments. Develop real-time multilingual audio intelligence pipelines with transcription, speaker attribution, timestamp alignment, and tone analysis while maintaining reliability, observability, cost efficiency, and data quality.

Build large-scale data systems for frontier coding-model pretraining across data quality, data platform, or web crawling. Responsibilities include developing high-throughput pipelines, training and deploying data-quality models, orchestrating reproducible datasets, improving crawl coverage and parsing, and hardening distributed infrastructure. The role requires independent end-to-end ownership, performance optimization, observability, experimentation, and close collaboration with research, training, acquisition, and engineering teams.

Design and build cloud cost-management capabilities, including forecasting, anomaly alerts, cost allocation, data pipelines, transformation models, visualizations, and reusable UI components. Own features end to end from planning through release while improving application reliability and performance. Collaborate closely with engineers and founding team members, contribute to technical design and roadmap decisions, and work pragmatically in a fast-moving distributed startup environment.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.