Top Data Engineer Jobs in Portugal

Develops and supports complex ETL pipelines using Azure and enterprise data integration tools. Responsibilities include extracting, transforming, and loading data from multiple sources; building cloud and on-premises connectivity; managing data governance and quality; optimizing workflows; monitoring pipelines; writing SQL programs and reports; analyzing system changes; and advising technical and executive stakeholders on data processing solutions.

Leads the design and delivery of AI-ready biologics discovery data products, scientific data models, integration requirements, and analytics-ready datasets. Translates scientific needs into data specifications, contracts, schemas, and acceptance criteria. Establishes data quality, metadata, lineage, provenance, FAIR data, and interoperability standards while partnering with scientists, AI/ML teams, architects, and technology partners. Provides technical leadership across complex initiatives and helps shape enterprise scientific data architecture.

Develops, maintains, tests, and supports enterprise applications and integrations within WMATA’s Digital Modernization portfolio. Builds scalable data pipelines and cloud-based solutions using OCI, Azure, Lakehouse architecture, and Autonomous Data Warehouse. Provides technical leadership, resolves complex system problems, manages configuration and version control, performs failure analysis, coordinates projects, prepares technical reports, and supports system reliability, production monitoring, and emergency outages.

Manage Microsoft Fabric dataflows, pipelines, APIs, external integrations, error reporting, and Azure capacity. Build and maintain scalable data warehouse infrastructure, optimize data access, and support AI and machine learning workloads. Document processes and operational best practices while collaborating with data engineers, BI analysts, and AI specialists.

Build and maintain production-grade ETL/ELT pipelines across legacy systems, AWS, Azure, and Snowflake. Develop feature stores, optimized datasets, and vectorization pipelines supporting machine learning and generative AI. Design cross-platform orchestration, data quality, observability, and governance frameworks while optimizing performance and cloud costs. Partner with MLOps, data science, infrastructure, security, and database teams to deliver scalable AI-ready data solutions.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before supporting client data solutions projects. Responsibilities include collaborating across teams, solving complex problems, managing priorities, meeting deadlines, and developing consulting expertise. Candidates should have programming experience, SQL or NoSQL knowledge, willingness to travel extensively, and existing U.S. work authorization. Data platform and cloud experience are preferred.

Build and maintain scalable batch and streaming data pipelines supporting analytics, machine learning, and business operations. Develop reliable data systems using Python, SQL, and data engineering tools; monitor and troubleshoot pipelines; implement data quality, observability, and testing practices; document architectures and transformations; and collaborate with analysts, data scientists, product managers, and engineering teams in an agile environment.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Supports data pipeline development and maintenance for a VA disability claims program. Responsibilities include preparing and cleansing healthcare data, troubleshooting ETL/ELT pipelines, performing data quality checks, documenting workflows, participating in code reviews, and learning claims data systems under senior engineer mentorship.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Build and operate data pipelines that integrate customer financial data from ERPs, warehouses, and payment systems. Develop AI agents that automate ERP implementations, partner with implementation teams to understand and transform customer data, and contribute to shared AI platform capabilities. The role requires strong full-stack engineering fundamentals, autonomous execution, and comfort working in an ambiguous, fast-moving environment.

Manage data engineering and analytics initiatives spanning SQL analysis, Power BI dashboards, dimensional data modeling, cloud data warehouses, and ETL pipelines. Partner with business stakeholders to translate questions into actionable insights and communicate findings clearly. Apply AI-assisted tools and evaluate LLM-based analytics capabilities such as natural-language querying, automated insight generation, and anomaly detection.

Build customer-facing data products for commercial vehicle customers, including forecasting, pricing signals, recommendations, matching, ranking, enrichment, and market intelligence. Apply LLMs and machine learning to messy structured and unstructured data, while creating evaluation, monitoring, and guardrail systems. Design ingestion, transformation, feature, entity-resolution, and data-quality pipelines. Partner with product, design, customers, and commercial teams, define success metrics, ship reliable solutions, and mentor analysts and engineers.

Build and scale batch and streaming data pipelines using Databricks, Spark, Kafka, AWS, Python, SQL, dbt, and Fivetran. Develop ETL/ELT and reverse ETL workflows, curated datasets, dashboards, data quality controls, and data activation integrations with Salesforce, HubSpot, and Braze. Partner with Finance, Marketing, Product, and Engineering on reporting, forecasting, segmentation, and AI/ML applications, including RAG, embeddings, and agent-based systems.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Entry-level systems engineer supporting spacecraft command and data handling networks for autonomous navigation satellite programs. Responsibilities include requirements definition and flowdown, subsystem design, interface development, trade studies, system performance analysis, integration, verification and validation, technical issue resolution, and mission assurance. The role collaborates across software, hardware, cybersecurity, communications security, and spacecraft testing disciplines, and requires eligibility for a U.S. Secret clearance.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Build and maintain large-scale data pipelines and platforms using Python, PySpark, and AWS data services. Optimize data processing performance, troubleshoot technical issues, and support scalable data engineering solutions. Requires a bachelor’s degree in engineering, data science, IT, or a related field, along with 0–2 years of experience or recent graduation.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Own the company’s data infrastructure, including ETL/ELT pipelines, clean analytical tables, data quality, schema management, freshness monitoring, lineage, and alerting. Partner with engineering, product, ML, data science, and go-to-market teams to improve instrumentation and deliver reliable product and operational insights. This high-scope role requires hands-on technical leadership, data governance, and scaling analytics capabilities in a rapidly changing environment.

As a Data Engineer at Whatnot, you'll design data architectures, implement data workflows, ensure data quality, and support analytics across teams.

Lead data engineering role responsible for designing, developing, testing, and supporting cloud-based data solutions. The position uses Python, Spark, Java, Scala, AWS, distributed computing, real-time streaming, NoSQL databases, and data warehouses. Responsibilities include collaborating across Agile teams, building scalable systems, conducting code reviews and unit tests, optimizing performance, partnering with product managers, staying current with emerging technologies, and mentoring engineers.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data and full-stack solutions. The role uses Java, Scala, Python, databases, distributed computing, streaming technologies, and cloud data warehouses. Responsibilities include collaborating across Agile teams, developing scalable solutions, conducting unit tests and code reviews, optimizing performance, keeping current with emerging technologies, and mentoring engineers.

Lead a large engineering organization to design, pilot, and roll out reference architectures and AI-ready data platform capabilities. Build and operate cloud-native data pipelines, feature and vector stores, observability and governance standards, and mentor senior technical leaders while driving enterprise-wide architectural modernization.