Top Data Engineer Jobs in Portugal

Designs, builds, and deploys Python-based NLP and machine learning workflows, knowledge graphs, analytical tools, APIs, and collaborative TypeScript applications. Integrates data feeds, supports secure end-to-end pipelines, prototypes solutions in ambiguous environments, and partners with Joint Staff customers in classified SIPRNet and JWICS environments. The role requires adapting technologies to mission-specific analytical and operational needs while providing documentation and technical support.

Designs, builds, and operates production data pipelines on Databricks and Azure using Python, SQL, Spark, Delta Lake, dbt, and Dagster. Responsibilities include ingestion, medallion-layer transformations, dimensional modeling, ML datasets, data contracts, quality testing, governance, CI/CD, observability, performance tuning, troubleshooting, and on-call support. The role partners with architects, analysts, product owners, and business stakeholders to deliver reliable, reusable data products.

Build and maintain modern data-centric applications, pipelines, transformations, enrichment processes, provisioning layers, and user interfaces supporting clinical and operational healthcare processes. Analyze clinical data and report trends using cloud, big data, data science, and modern software development practices. Collaborate with Product, Platform, and Architecture teams, mentor Data Engineers, document solutions, manage source control, and research emerging technologies.

Builds and optimizes enterprise quality data solutions, including pipelines, integrations, databases, dashboards, analytics platforms, and cloud technologies. Partners with Quality, IT, business teams, and vendors to translate requirements into scalable data, automation, reporting, and AI solutions. Governs data management, security, compliance, and lifecycle practices; supports quality systems and users; analyzes quality trends; and drives continuous process improvement. The role also leads AI-enabled solutions using Microsoft Copilot Studio, AI Builder, and Power Platform.

Designs and scales back-end data-processing systems for critical mission platforms. Responsibilities include high-volume dataflow reliability, performance tuning, distributed-system resilience, data modeling, observability, failover, and large-scale query optimization using Kafka, Elasticsearch, and PostgreSQL. The role supports cyber mission and digital network intelligence workflows, with potential work involving Go services, microservices, container orchestration, and CI/CD pipelines.

Build and operate reliable data infrastructure, including event-driven architectures, streaming and ingestion pipelines, API integrations, and automated workflows. Develop agentic systems that interpret events and trigger business actions, while ensuring data quality, observability, error handling, and safe execution. Partner with analytics teams, contribute to data architecture strategy, and manage multiple engineering projects. The role requires strong Python expertise and experience with real-time data systems, orchestration, and integrations.

Conduct flight test engineering for aircraft platforms, including test planning, requirements and configuration review, safety planning, test procedures, control room support, real-time decision-making, and pre/post-test data analysis. The role coordinates with design, maintenance, contractors, and customers; supports readiness reviews and test operations; maintains testing metrics; and requires occasional overtime, weekend, and odd-shift work. The position is fully onsite at Edwards AFB and requires an active Secret clearance plus SAP eligibility.

Build and operate governed production data pipelines in Databricks using medallion architecture, Unity Catalog, data quality controls, lineage, and semantic models. Develop AI agents and retrieval-augmented applications grounded in enterprise data using Mosaic AI and model serving. Integrate enterprise systems through APIs and events, implement security and observability, and own architecture, testing, CI/CD, deployment, and production support.

Operate and improve the reliability, observability, and deployment safety of cloud, Kubernetes, application, and data-platform environments. Responsibilities include incident response, troubleshooting Databricks and Spark workloads, maintaining dashboards and alerts, supporting streaming and orchestration systems, automating operational tasks, improving release processes, and partnering with engineering teams on platform modernization and post-incident corrective actions.

Build and maintain versioned data ingestion and conflation pipelines that transform complex retail and spatial data into accurate, reliable insights. Responsibilities include entity resolution, golden-record creation, rules-based normalization, RegEx logic, LLM integration, schema evolution, and cost-effective scaling. The role emphasizes engineering rigor, high-fidelity outputs, and production-ready data for consumer dashboards.

Designs, develops, and optimizes scalable data models, cloud-native platforms, and batch and real-time pipelines for an AI/ML disability claims program. Responsibilities include data quality, governance, security, database optimization, ETL automation, observability, ML pipeline enablement, and cloud migration. The role mentors engineers, leads cross-functional initiatives, documents architecture, and ensures reliable, high-performance data systems supporting analytics and machine learning.

Conduct engineering studies, collect and analyze project data, prepare scopes, plans, cost estimates, and technical reports. Coordinate with manufacturers, inspectors, fabricators, clients, and technical experts while managing multiple projects and providing professional customer service. Work may involve design, materials, mechanical, electrical, or structural engineering in office and laboratory environments, with some travel required.

Architect and build Astranis’s enterprise data platform, including ingestion, storage, analytics pipelines, unified logging, observability, governance, RBAC, and schema evolution. Partner with teams across aerospace, manufacturing, supply chain, finance, sales, and software to develop production-ready backend tools. Integrate telemetry, ERP, factory, and operational data using modern databases, warehouses, orchestration tools, and cloud infrastructure.

Leads the architecture, development, and operation of scalable cloud data platforms and complex ETL/ELT pipelines. Partners with senior stakeholders to define data strategy, architectures, and technical roadmaps. Mentors data engineers, establishes engineering and governance standards, and owns production reliability, data quality, observability, and incident response. Evaluates generative AI and agentic systems for data engineering while promoting responsible AI practices. Requires advanced Python, SQL, cloud, distributed processing, integration, DevOps, and data architecture expertise.

Designs and maintains scalable ETL pipelines for structured and unstructured data, supporting DEA legacy-data migration, validation, integration, and modernization. The role improves data quality, interoperability, reliability, and cloud-platform performance while preparing data for analytics and AI/ML use cases. Responsibilities include source-to-target mapping, troubleshooting, documentation, stakeholder collaboration, and participation in Agile development.

Develops and maintains HCM dashboards, APIs, data pipelines, and analytical products for USDA modernization initiatives. Translates business requirements into secure, scalable data solutions; supports migration planning, data quality, governance, metadata, systems integration, testing, validation, and Databricks Lakehouse architecture. Responsibilities also include requirements analysis, UAT coordination, documentation, training, and lifecycle management of HCM data products.

Build and maintain scalable computer vision data pipelines for image, video, and metadata assets. Develop ETL workflows using NiFi and Prefect, media-processing services with Java, FFmpeg, and OpenCV, and containerized services with Docker. Collaborate with computer vision, MLOps, and platform teams while optimizing performance, cost, reliability, monitoring, logging, testing, and data quality. The role requires an active TS/SCI clearance and ability to obtain a polygraph.

Build, deploy, and maintain reliable data infrastructure, including ingestion and ETL/ELT pipelines, data quality processes, warehouses, automation, orchestration, and lightweight data applications. Optimize data stack performance, document pipelines, and collaborate with analysts, data scientists, and stakeholders. Curate datasets supporting operational models, feature stores, and AI/ML workflows while continuously learning data engineering best practices.

Build scalable data processing and exploration pipelines, ML infrastructure, and cloud-based applications. Write, test, instrument, and deploy software in Kubernetes environments while contributing to distributed systems and data-driven products. The role focuses on Python, big data technologies, containerization, and systems supporting marketplace intelligence.

Designs and operates production data infrastructure, ETL/ELT pipelines, APIs, containerized AWS services, and graph-based systems. Responsibilities include relational, document, and graph database architecture; data quality and lineage; CI/CD automation; and integration of blockchain, financial, and investigative data. Partners with R&D, data scientists, and investigations teams to deliver production software for federal and commercial clients.

Develops production-ready AI, data automation, integration, and workflow solutions for engineering processes in an aerospace and defense environment. Responsibilities include programming, API and database integration, engineering toolchain integration, CI/CD, testing, documentation, workflow analysis, roadmap development, training, and user support. The role collaborates with systems engineers and customer teams and may deploy tools in classified environments.

Designs, builds, and maintains large-scale distributed data systems and pipelines supporting blockchain analytics products. The role involves batch and streaming data solutions, cloud infrastructure, workflow orchestration, reliability, observability, cost optimization, peer review, and technical roadmap development. The engineer collaborates across product, data science, blockchain, and intelligence teams while mentoring other engineers and applying AI tools effectively.

Build and operate a self-service data platform supporting databases, messaging, ingestion, processing, and consumption at scale. Develop secure, compliant, and scalable infrastructure using Infrastructure as Code and automation. Evolve support for structured and semi-structured data, stream processing, and governance. Work with distributed messaging systems, including Kafka and Flink, while mentoring teammates and partnering directly with stakeholders to drive engineering excellence.

Build and operate scalable AWS data platforms, batch and near-real-time pipelines, ETL/ELT workflows, and governed data lake or lakehouse architecture. Lead data integration for organizational acquisitions, improve data quality and observability, and support Power BI reporting through curated datasets and semantic models. Partner with technical and business stakeholders across editorial, subscriptions, advertising, finance, marketing, and operations to deliver reliable analytics and reporting foundations.

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.