Top Data Engineer Jobs in Portugal

Provides senior technical leadership for enterprise data engineering platforms, with emphasis on Snowflake, ETL/ELT pipelines, data quality, governance, observability, reliability, and production operations. The role guides architecture and engineering standards, supports incident response and on-call operations, improves platform scalability and resilience, and partners with engineering, infrastructure, security, governance, architecture, and business teams. It is a hands-on leadership position without direct people-management responsibility.

Provides electrical engineering support for data center design, construction, expansions, retrofits, and upgrades. Reviews designs, proposals, calculations, equipment submittals, construction deliverables, and standards. Performs power, load, arc flash, coordination, PUE, lighting, and underground heat analyses. Supports commissioning, factory testing, customer meetings, troubleshooting, value engineering, MOP reviews, and development of electrical and MEP standards.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Designs, builds, and optimizes scalable end-to-end data pipelines and curated data products using Snowflake, SQL, and Python. Translates business needs into reliable technical solutions, ensuring data quality, governance, performance, and cost efficiency. Troubleshoots complex issues, contributes to architectural decisions, uses AI-assisted engineering tools, and collaborates with engineering, analytics, product, and business stakeholders. The role also includes code reviews, documentation, mentoring, and influencing data engineering best practices.

Develop and manage data pipelines, integrations, analytics solutions, ontologies, business object models, and enterprise data architectures using Palantir Foundry. The role supports data governance, quality, lineage, operational workflows, technical solution design, and cross-functional collaboration with technical and business stakeholders. Federal or Department of Defense experience and Agile experience are preferred. A DOD Secret clearance is required.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data warehouses supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains SSRS reports, implements data quality controls, migrates legacy systems, and collaborates with business and technical teams. The role also promotes AI-assisted development practices and documents technical procedures.

Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams.

Design, build, deploy, and operate production-grade data pipelines and products for healthcare and marketing datasets. Implement data models, Airflow workflows, data-quality controls, and PHI/PII privacy controls. Collaborate with product, analytics, and platform teams to support audience discovery, segmentation, activation, measurement, and reporting.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Design and deliver blockchain network infrastructure and platform APIs, lead multi-quarter initiatives (chain integrations, re-architecture, data migrations), define APIs/SLOs/observability and on-call ownership, ship SDKs and platform primitives, establish data contracts, and simplify cross-chain operations to enable analytics, ML, and data engineering teams.

A 12-week internship focused on building data pipelines, machine learning workflows, and distributed systems. The intern will use Python, ML frameworks, Spark, Docker, and Kubernetes to develop, train, deploy, and scale models and intelligent data solutions. Responsibilities include supporting reliable, performant AI products and gaining experience with cloud platforms, scalability, fault tolerance, and production ML deployment.

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

Validates complex enterprise data processing, transformations, migrations, reconciliations, reporting, business rules, APIs, and event-driven workflows. Develops data validation strategies, automated database validation scripts, migration tests, and reconciliation reports. Partners with data architects and engineers while mentoring junior testers. The role requires advanced SQL, automation, ETL validation, relational data modeling, large-scale migration testing, and Python programming.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Senior Data Engineer responsible for designing and deploying scalable data pipelines, ensuring critical data availability, movement, transformation, and storage. The role requires Python, Databricks, ETL, relational database, and Agile Scrum experience, supporting AI and machine learning applications for national security missions. The position is primarily onsite three to five days per week and requires an active Secret clearance and U.S. citizenship.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Designs and leads cloud-native data engineering and architecture solutions for Supply Chain analytics. Responsibilities include building AWS data pipelines and lakehouse platforms, implementing Apache Iceberg and Medallion Architecture, developing Redshift data products, applying data mesh and governance principles, integrating SAP and Blue Yonder data, and leading technical engineering teams.

Build and operate scalable backend infrastructure for financial data, including ingestion, transformation, integration, and serving systems. Design high-performance distributed pipelines handling millions of documents and datapoints, support AI and retrieval systems, and integrate heterogeneous financial sources. Ensure secure, compliant, reliable handling of customer data while collaborating with globally distributed engineering teams and influencing technical direction.

Build and integrate deep learning models into software pipelines, collaborate with product and engineering teams, support data ETLs, and deploy solutions to cloud-hosted environments.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Develop and implement quality strategies, automated testing frameworks, and validation practices for data pipelines and analytical datasets. Test accuracy, completeness, integrity, schema consistency, business rules, and freshness using Python, PySpark, SQL, Spark, Airflow, and AWS. Investigate data anomalies and pipeline failures, monitor quality metrics, support CI/CD and releases, and collaborate with engineers, analysts, data scientists, and stakeholders to ensure reliable financial and regulatory data.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Build and operate Figma’s shared data platform supporting analytics, data science, and AI products. Responsibilities include improving development, testing, CI/CD, deployment, monitoring, data quality, incident response, performance, governance, lineage, and self-service data pipeline workflows. The role partners cross-functionally to establish reliable patterns for batch, micro-batch, reverse ETL, and product-facing data delivery.