Top Data Engineer Jobs in Portugal

Designs, develops, tests, deploys, and monitors scalable data pipelines using Azure Data Factory, Databricks, Python, SQL, and dbt. Integrates source systems with data warehouses and other platforms, automates data processes, improves infrastructure scalability, and supports data migrations during mergers and acquisitions. Ensures data security, privacy, retention, and effective enterprise data management.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines. Ingest, transform, and analyze structured and unstructured data supporting analytics, machine learning, and reporting. Develop reliable production systems using Spark, streaming technologies, SQL, orchestration tools, and distributed systems principles. Troubleshoot, document, and optimize data workloads for performance and cost. Preferred experience includes cloud Hadoop platforms, lakehouse formats, governance tools, Kubernetes, CI/CD, and infrastructure as code.

Define and lead enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Establish data modeling, security, lifecycle, and platform standards; architect lakehouse, warehouse, and streaming solutions; integrate governance and catalog tools; design scalable pipelines, disaster recovery, and high availability; optimize costs; guide architecture reviews; partner with ML, BI, and product teams; and mentor data engineers and architects.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

Lead Data Privacy Engineer responsible for embedding privacy principles, ensuring regulatory compliance, and leading data protection initiatives across systems and cross-functional teams.

Lead design, deployment, and operational support for enterprise data protection across hybrid cloud and on-prem. Focus on encryption, tokenization, key management, HSM operations, integrations with apps and storage, automation via IaC, incident response, vendor strategy, and troubleshooting. Partner with application and infrastructure teams to ensure secure, stable production data protection and compliance.

Leads development and technical direction for a cloud-native Master Data Hub supporting trusted enterprise data. Designs applications, APIs, data models, integrations, pipelines, and automation; improves reliability, security, monitoring, and operational efficiency. Mentors developers, influences architecture, troubleshoots complex issues, manages releases, and partners with commercial, governance, regulatory, supply chain, finance, and technology teams to enable commercialization, analytics, AI, and digital commerce.

Build and operate AWS-based data platforms, Java/Spring Boot services, reliable data pipelines, and AI-powered applications. Responsibilities include data ingestion and modeling, predictive ML, generative AI and RAG with Bedrock and OpenSearch, production monitoring, security, CI/CD, infrastructure as code, and integrations with databases, POS, payment, and partner systems.

Designs, develops, tests, deploys, and supports enterprise data products, pipelines, transformations, and analytics solutions using Databricks and cloud technologies. Builds governed Bronze, Silver, and Gold lakehouse assets; implements data quality, monitoring, security, and access controls; and develops reusable data models and engineering components. Collaborates with architects and Agile delivery teams, participates in reviews and CI/CD, troubleshoots performance issues, automates operational processes, and documents technical solutions. The role supports reporting, operational workloads, and AI/ML use cases.

Build and modernize Carrum Health’s data platform by developing Python and SQL pipelines, AWS services, Terraform infrastructure, dbt transformations, observability, testing, and CI/CD. Automate carrier integrations, reduce operational toil, improve reliability, and support Databricks adoption. Extend AI-assisted ingestion and classification using Bedrock and collaborate across engineering, analytics, data science, and client-facing teams. The role includes production ownership, incident response, technical documentation, and healthcare data platform development.

Build and improve data products, reporting layers, integrations, workflow tools, dashboards, and product-facing data features. Translate ambiguous business needs into maintainable technical solutions, improve data reliability and accessibility, and support analytics, experimentation, segmentation, personalization, and reporting. Collaborate with engineering, product, marketing, e-commerce, and business systems teams while documenting data logic and identifying reusable capabilities.

Build and maintain cloud data architectures, SQL Server databases, data pipelines, models, Power BI solutions, and .NET applications. Lead database modernization and Azure migrations, implement security and DevOps practices, integrate APIs, apply AI and advanced analytics, and ensure data quality, governance, reliability, and cost efficiency. Collaborate with stakeholders and produce technical documentation.

Build and maintain scalable data pipelines and distributed processing solutions supporting analytics, reporting, and machine learning. Develop cloud-based Big Data platforms using Python, Spark, Hadoop, and AWS or GCP. Apply data quality, governance, monitoring, and automation practices; collaborate with architects, data scientists, and analytics teams; participate in Agile delivery and production support; and mentor junior engineers.

Designs and implements scalable software solutions, develops maintainable features, resolves bugs, optimizes performance, and writes automated tests. Participates in architecture discussions, code reviews, Agile planning, CI/CD, observability, and adoption of AI-assisted engineering tools. Collaborates with QA, Product Management, and DevOps to deliver software across web, desktop, cloud, and IoT environments. The role is remote for candidates in the Seattle or Los Angeles metropolitan areas, with quarterly travel to Los Angeles.

Build and launch production data systems and pipelines, provision dedicated AWS environments with infrastructure-as-code, and design connected data models with traceability and access controls. Own ambiguous engineering problems from concept through dependable production use, collaborate on architecture, and use AI and LLM-assisted tools extensively. The role requires independent work in a lean team and five days per week onsite in New York.

Lead the Ads Signals & Measurement data engineering team across conversion and attribution, measurement foundations, audience onboarding, and identity. Set cross-organizational technical direction, resolve scalable data architecture decisions, mentor engineers, and partner on platform-wide initiatives. The role requires expertise in modern data stacks, advertising technology data flows, privacy and governance, distributed systems, and emerging AI technologies.

Build and optimize marketing data platforms, applications, pipelines, algorithms, and tools. Analyze complex datasets using cloud technologies to support marketing optimization and business objectives. Develop reliable batch and streaming ETL/ELT pipelines, integrate data sources through APIs, and collaborate with engineering and marketing science teams to improve processes and discover new data sources.

Build and operate Python backend services, REST APIs, clinical data models, and batch or event-driven pipelines. Work with FHIR and HL7 healthcare data to support reliable integrations, reporting, analytics, and AI workflows. Establish data quality, validation, governance, lineage, monitoring, and auditability practices. Lead architectural discussions, review implementations, mentor engineers, and collaborate with clinical, product, analytics, and AI teams.

Design, build, and maintain scalable data platforms and ETL/ELT pipelines using Databricks/Spark and AWS. Develop backend data services in C#/.NET, ensure data quality and performance, leverage AI-assisted coding tools, collaborate with cross-functional teams, lead technical design decisions, and mentor engineers.

Develop and optimize large-scale data systems, ETL pipelines, storage, entity-resolution products, and data automation infrastructure. Build and integrate ingestion and processing pipelines using AWS, Airflow, Spark, PySpark, and EMR. Create testing and monitoring components, develop analytical tools, support data governance and quality, collaborate with data science teams, and document technical solutions.

Lead the modernization of Ampacity’s data platform by designing and implementing an Azure and Databricks lakehouse. Responsibilities include migrating Azure Data Factory pipelines, developing Delta Lake architectures, building SQL and Python transformations, establishing engineering standards, monitoring data quality, leading technical projects, mentoring engineers, and supporting team growth.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements for cost efficiency, reliability, access control, observability, orchestration, CI/CD, and developer experience. Drive complex cross-team initiatives, resolve production issues, influence architectural decisions, mentor engineers, and build automation incorporating AI into data engineering workflows.

Develop and maintain interactive BI dashboards, reports, data models, and pipelines using Power BI, Tableau, and Amazon QuickSight. Translate stakeholder requirements into visualization solutions, perform ETL and administrative tasks, manage data connections and governance, and provide user support and training. Create documentation and collaborate with federal stakeholders to deliver real-time organizational insights.

Lead architecture and development of Beacon’s backend data platform, including PostgreSQL data models, Kafka event pipelines, GraphQL APIs, clinical data access controls, and warehouse snapshot pipelines. Design complex systems, tune queries, operate production services, debug cross-system issues, write RFCs, mentor engineers, improve tooling and documentation, and collaborate with scientific, clinical, product, and engineering stakeholders.

Leads Knowledge Management, operational intelligence, service reliability, continuous improvement, and data governance across MedTech R&D applications. Develops dashboards, analyzes service and support data, improves CMDB quality, identifies reliability risks and automation opportunities, and supports AI-ready knowledge foundations. Partners with product, operations, reliability, support, and vendor teams to improve service quality, self-service adoption, issue resolution, operational efficiency, and application portfolio governance.