Top Data Engineer Jobs in Portugal

Leads software engineering for data platform and entitlement solutions within an agile financial services technology team. Designs, develops, troubleshoots, and maintains secure, scalable Java and Spring applications using microservices, Kafka, cloud platforms, and CI/CD. Creates architecture artifacts, develops relational data models, improves system architecture, and promotes responsible AI-assisted engineering practices. Preferred experience includes modern data platforms, Spark, IAM, authorization models, and enterprise AI integration.

Develop and support software solutions within an Agile Scrum team. Responsibilities include designing, coding, testing, debugging, enhancing existing applications, conducting code reviews, analyzing requirements, resolving customer software issues, integrating new technologies with cloud infrastructure, and communicating project updates and risks to stakeholders.

Designs and maintains enterprise data architecture, pipelines, ETL/ELT workflows, and financial data integrations using Snowflake, AWS, dbt, Informatica, and relational databases. Ensures data quality, develops reporting and dashboards, supports financial planning and analysis, and delivers technology projects end to end. Partners with Finance, Accounting, FP&A, and technology teams, contributes to Agile planning, mentors junior staff, applies AI-assisted tools, and improves data processes and analytical capabilities.

Designs, builds, and optimizes enterprise data pipelines, architectures, and applications supporting financial consolidation, planning, forecasting, reporting, and analytics. Develops ETL/ELT workflows, data models, quality checks, dashboards, and integrations across cloud and relational platforms. Manages projects, partners with Finance and technology teams, improves processes, mentors junior staff, contributes to Agile planning, and evaluates emerging AI and data technologies. Rotational after-hours on-call availability may be required.

Build and operate analytics pipelines, data models, data marts, and quality systems for mobile free-to-play game telemetry. Maintain and optimize BigQuery transformation layers, migrate Airflow pipelines to a partner-managed ETL platform, define tracking plans and data contracts, and develop observability capabilities. Partner with studios and analysts to support retention, monetization, and engagement reporting while applying production engineering, documentation, and collaboration practices.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

The Data Engineer will design and develop Java-based applications, optimize APIs, and collaborate with teams, focusing on cloud integration and SQL queries.

Design and develop AWS-based data platforms, build and maintain ETL pipelines using PySpark, perform data modeling and governance, integrate event-driven systems with Kafka, manage containerized deployments with Docker and Kubernetes, and produce clear documentation while collaborating with stakeholders.

Administer, architect, optimize, secure, and automate PostgreSQL, MySQL, and Snowflake databases in Azure. The role develops resilient database solutions for healthcare applications, manages performance, access controls, encryption, backups, and disaster recovery, and creates technical documentation. It collaborates across Scrum teams, presents solutions to stakeholders, evaluates emerging technologies, uses approved AI tools, solves complex database challenges, and mentors junior engineers.

Develop, maintain, and support scalable data systems and pipelines for analytics and reporting. Collect, clean, structure, enrich, and govern data from multiple sources while ensuring quality, security, reliability, compliance, and performance. Collaborate with analysts, business analysts, and data scientists to design solutions, deploy optimized pipelines, troubleshoot data issues, and provide technical support.

Designs, implements, and operates data management systems supporting defense intelligence needs. Develops data architectures, structures, and integration strategies; optimizes data storage, throughput, and query performance; collaborates with data users and intelligence community members; and contributes to complex analytical products and information-sharing efforts.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

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.