Top Data Engineer Jobs in Portugal

Build secure, scalable data platforms and pipelines supporting product and customer needs. Develop robust, tested Java and Python code; design distributed data systems, modern SQL/NoSQL structures, data lakes, and warehouses; collaborate with business and technology stakeholders; document solutions; and use AI-powered development tools throughout the software development lifecycle.

Own the architecture and development of data-intensive web applications powering customer insights. Provide hands-on technical leadership, mentor engineers, conduct architecture and code reviews, resolve complex distributed-system issues, and deliver performant, maintainable data products. The role involves ETL and/or RAG development, full-stack work across React, TypeScript, Next.js, PostgreSQL, and C#/.NET, with opportunities to operate cloud infrastructure and observability systems.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Build and optimize scalable data pipeline architecture, assemble complex datasets, improve data delivery infrastructure, and support data initiatives across engineering, product, BI, and data science teams. The role requires production-scale software engineering experience focused on data and ML processes, expertise with Spark, Kafka, SQL, and a programming language such as Python, Scala, or Java, plus knowledge of databases, data modeling, and ML operations.

Designs, develops, tests, deploys, and maintains big data software systems processing network, DNS, and web content data. The role requires Java or Python development, distributed systems, data storage, performance tuning, testing automation, CI/CD, cloud-native architectures, incident response, and technical leadership in an Agile environment.

Leads the architecture, development, governance, and adoption of Growth[x]'s enterprise data and AI foundation. Responsibilities include building knowledge cortexes, integrating governed data sources, improving data quality, designing AI-enabled workflows, productizing prompts and playbooks, developing training, managing a technical team, and coordinating with Growth leadership, IT, security, and platform stakeholders. The role is hands-on and requires expertise in agentic AI, data engineering, cloud platforms, data warehousing, BI, and enterprise solution architecture.

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 maintains scalable data pipelines, warehouse solutions, semantic models, dashboards, and reporting environments using GCP, BigQuery, Looker, and related technologies. Troubleshoots production data issues, improves data quality and processing efficiency, documents workflows, supports BI users, contributes to coding standards, and safeguards healthcare and PHI data. Collaborates with data scientists, analysts, and business stakeholders while using AI tools to improve workflows and decision-making.

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.

Develop and manage enterprise data platforms, pipelines, APIs, MDM/RDM solutions, governance frameworks, and AI-enabled observability for semiconductor supply chain data. Integrate SAP, PLM, planning, and business systems while building canonical models, metadata catalogs, lineage, data quality processes, and agentic AI workflows. Partner with engineering, business, and IT stakeholders to deliver scalable, governed data solutions across manufacturing and supply chain operations.

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.