Top Data Engineer Jobs in Portugal

Develop and maintain Java applications, RESTful APIs, backend services, and cloud-ready integrations using AWS. Write optimized SQL, automate tasks with Bash, manage code with Git, troubleshoot production issues, and participate in architecture discussions, code reviews, and continuous improvement. The role requires ownership of end-to-end technical solutions and collaboration across teams.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Build and maintain Databricks data pipelines using SparkSQL and PySpark, integrating payer, provider, claims, platform, and API data. Develop transformations, models, file exchanges, dashboards, automated workflows, tests, and data-quality validations. Collaborate in design and code reviews, use AI coding tools responsibly, and protect PHI through governance, access controls, and audit logging.

Own and define Dubsado’s data infrastructure strategy, including pipelines, warehouse reliability, analytical modeling, data quality, observability, and tooling standards. Build scalable data systems, collaborate with engineering, product, and business teams, and support AI agents, customer-facing features, and internal users. Serve as the initial dedicated data specialist, operating autonomously while gradually distributing data quality ownership across engineering.

Designs, builds, deploys, and maintains enterprise-scale Azure cloud data solutions and healthcare analytics platforms. Responsibilities include developing data pipelines and warehouses with Databricks, Snowflake, Kafka, and Azure Data Factory; applying AI productivity tools; evaluating emerging technologies; enforcing governance, testing, security, and quality standards; and optimizing performance. The role collaborates with product and engineering teams to deliver commercially available healthcare software and supports scalable data architecture.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead architectural decisions, evaluate platforms such as Snowflake and Databricks, establish engineering patterns, and deliver resilient data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical outcomes to stakeholders. Drive large-scale data initiatives independently in an Agile environment.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with engineering, product, analytics, and data science teams; develop distributed and real-time data workloads; enforce reusable engineering patterns; implement security and access controls; optimize resilience and operational efficiency; communicate technical outcomes; and mentor data professionals.

Design, architect, develop, and test a high-capacity data and video system for the Rocky program. Lead UI design, translate requirements, perform system-level analyses, oversee code reviews, and research/implement emerging software technologies and development practices to meet program milestones.

Designs, builds, enhances, and integrates data center infrastructure supporting enterprise platforms. Responsibilities include infrastructure solution delivery, test planning, network implementation support, troubleshooting server, storage, mainframe, wired and wireless issues, asset and inventory management, and customer communication. The role operates in a 24/7 environment, may require nights, weekends, and holidays, and requires lifting up to 50 pounds.

Develop and deploy AI-powered annotation, labeling, and captioning systems for robotics, computer vision, autonomy, and VLA datasets. Build data pipelines supporting human review, quality validation, and dataset publication; train and evaluate machine learning models; integrate Caterpillar systems with annotation providers; and ensure scalable, observable production services for heavy-equipment and construction environments.

Develop and deploy AI-driven annotation, labeling, and captioning systems for robotics, computer vision, autonomy, and Vision-Language-Action datasets. Train and evaluate machine learning models, build scalable data pipelines, integrate human-in-the-loop workflows, and improve annotation quality. The role requires production software engineering, distributed systems, sensor-data expertise, and experience with computer vision or autonomous systems. Work is onsite in Irving, Texas, with relocation assistance and visa sponsorship available.

Build and maintain production data pipelines, integrations, semantic models, AI agents, copilots, and automated workflows using business data. Partner with Sales, Marketing, RevOps, and Customer Success to deliver reliable analytics and automation solutions. Establish data quality, monitoring, evaluation, access controls, and deployment practices while enabling colleagues to adopt AI tools effectively.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Leads development of scalable ML and AI platforms, contributing Python code and owning delivery from architecture through production operations. Builds data transformation, feature management, orchestration, model serving, APIs, SDKs, and agentic AI capabilities. Automates infrastructure and CI/CD with AWS, Terraform, containers, and Databricks. Ensures reliability, observability, security, performance, and cost efficiency while mentoring engineers and partnering with data science, ML, and product teams.

Build and maintain recruiting data pipelines, datasets, integrations, dashboards, automations, and data quality controls. Partner with recruiting, data, security, privacy, engineering, and business teams to improve operational reporting and workflows. Responsibilities include integrating ATS and HR systems, monitoring pipeline stability, supporting writeback processes, designing secure access solutions, documenting sensitive workflows, and adapting data products as recruiting processes evolve.

Lead data engineering efforts supporting secure, scalable data collection, storage, access, and analytics platforms. Responsibilities include building data models, maintaining critical pipelines and architectures, implementing database backup, recovery, and archiving strategies, and reviewing data security and access controls. The role also applies and validates enterprise-authorized AI capabilities in data engineering workflows, requiring expertise across the data lifecycle, relational and NoSQL databases, and advanced statistical, geometric, and linear algebra algorithms.

Own the architecture, development, and operations of Cargill’s enterprise data platform. Build and optimize Azure and on-premises ETL/ELT pipelines, manage databases and migrations, implement CI/CD, improve performance and reliability, and create embedded Superset dashboards. Monitor platform health, data quality, security, and costs while integrating emerging AI capabilities. Mentor data professionals and make architectural decisions across global teams.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure and resilient data solutions; enforce reusable engineering patterns; communicate technical outcomes; mentor colleagues; and support distributed, real-time, and cloud-based workloads.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead transformative data initiatives, make architectural decisions, evaluate platforms such as Snowflake and Databricks, and ensure reliability and maintainability. Collaborate with product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders. Apply data modeling, relational and NoSQL databases, machine learning-enabled solutions, and modern cloud architectures to support risk management products.

Process and validate aircraft monitoring data, decode avionics bus data, troubleshoot C-17 systems, perform root cause analysis, develop data-mining algorithms, and provide technical recommendations to customers. The role supports program reviews, integrated product teams, design reviews, and aircraft health management for the C-17 fleet.

Leads a scrum team developing AI-driven annotation automation for robotics, autonomy, computer vision, and VLA datasets. Designs and deploys automated labeling and captioning systems, trains and evaluates machine learning models, builds data pipelines incorporating human review and quality validation, integrates annotation providers, and improves service reliability and scalability. Provides hands-on technical leadership and mentorship while applying technologies such as NVIDIA Cosmos, computer vision, multimodal AI, and sensor-data workflows.

Develops and supports a full-stack agentic data quality platform within an agile engineering team. Responsibilities include secure software development, frontend and backend engineering, data platform development, AI agent and MCP server creation, platform automation, troubleshooting, testing, observability, and controlled releases. The role uses React or Angular, TypeScript, Python, Java or Kotlin, cloud and data technologies, and LLM capabilities to deliver autonomous, scalable solutions for Asset & Wealth Management.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, data systems, data models, and cloud-based data products. Develops automated deployment pipelines, evaluates architectures, supports data governance, and collaborates with analytics teams and stakeholders. The role requires advanced programming and SQL expertise, modern data architecture experience, and proficiency with cloud platforms, Spark, Kafka, orchestration tools, and data modeling frameworks.

Design, deploy, secure, automate, and support enterprise Azure and Azure Databricks data platforms. Responsibilities include infrastructure and networking engineering, Databricks administration, Infrastructure as Code, CI/CD and DevSecOps, identity and governance controls, observability, reliability, incident management, disaster recovery, performance optimization, and cloud cost management. The role also provides technical leadership through code reviews, documentation, mentoring, and collaboration with engineering and security teams.