Top Data Engineer Jobs in Portugal

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Build and operate scalable Python-first ETL and ELT pipelines for genomic and operational data. Integrate LIMS, laboratory instruments, applications, and third-party sources while guiding architecture and engineering standards. Own data quality, lineage, observability, reproducibility, audit readiness, security, and regulatory compliance across hybrid and cloud environments. Work with SQL databases, workflow orchestration, containers, Kubernetes, AWS data services, distributed processing, streaming, bioinformatics workflows, and infrastructure as code.

Designs, deploys, and optimizes secure enterprise database and data environments supporting mission-critical ArcGIS systems. Responsibilities include database architecture, administration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, cloud integration, and geospatial data support across cloud, on-premises, and hybrid infrastructures. The role also provides technical guidance and collaborates with technical teams, project managers, and customers throughout implementation projects.

Design, deploy, secure, and optimize enterprise database environments supporting mission-critical ArcGIS systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, data loading, and integration. The role operates across cloud, on-premises, and hybrid infrastructures and provides technical guidance to project teams and customers.

Designs, deploys, secures, and optimizes enterprise database and geospatial data platforms across cloud, on-premises, and hybrid environments. Responsibilities include database administration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, data integration, and technical guidance for mission-critical ArcGIS systems supporting national security organizations.

Define and advance Mastercard’s enterprise data architecture strategy across hybrid cloud and on-premises environments. Lead modernization of global data platforms, establish architecture standards, enable batch and real-time analytics, and promote Data Mesh and data-as-a-product capabilities. Ensure security, resiliency, governance, regulatory compliance, and scalability across AWS, Azure, and GCP. Advise executive stakeholders, evaluate emerging AI-enabled data technologies, and mentor global engineering teams.

Leads Mastercard’s enterprise data architecture strategy across hybrid cloud and on-premises environments. Defines secure, resilient, compliant architecture standards; modernizes legacy platforms across AWS, Azure, and GCP; and advances Data Mesh, real-time streaming, batch analytics, and AI-enabled data capabilities. Serves as a senior technical authority and executive advisor, evaluates emerging technologies, drives architecture governance, and mentors global engineering teams.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Entry-level rotational Data Engineer role focused on SQL Server development, SSIS package creation, data validation, ETL, performance tuning, and cross-platform integrations (Salesforce, Marketing Cloud). Participate in SDLC, code reviews, data profiling, reporting, and stakeholder meetings with the goal of permanent placement after rotations.

Design, build, and maintain cloud-native AWS data platforms and scalable data lake/warehouse solutions. Develop and deploy data pipelines for structured and unstructured data to support AI/ML and BI, lead cross-functional teams, and manage cloud migration and modernization efforts. TS/SCI with polygraph and a CS bachelors degree required.

Develop machine-learning models and analytical pipelines for space domain awareness, including trajectory classification, anomaly detection, benchmarking, calibration, and evaluation under sparse and noisy observations. Integrate models with astrodynamics and embedded systems, optimize inference for constrained onboard hardware, and support model lifecycle, retraining, systems engineering, and operational documentation.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Develop and maintain high-throughput backend and middleware systems for PlayStation’s cloud streaming data platform. Build Flink stream-processing and scalable ETL jobs, optimize data pipeline performance and reliability, support Kafka-to-cloud migrations, and contribute to architectural decisions. Analyze business requirements and collaborate with Business Intelligence and product teams to transform real-time and batch data into actionable insights.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

As a Senior Software Engineer on the Data Platform team, you'll enhance the data infrastructure, develop ETL pipelines, maintain data quality, collaborate across teams, and mentor junior engineers.

As a Data and Control Systems Engineer, you will design, build, and integrate data systems for testing rocket components, work closely with technicians, and collaborate with operations and software teams to automate hardware control.

Lead design and delivery of GTM-facing software and data products: build data foundations, pipeline visualization, sales tooling, and production services. Own end-to-end projects, participate in on-call, drive cross-functional initiatives, and leverage AI to accelerate development on Cloudflare's platform.

Build and maintain internal data application tools and frameworks for a centralized cloud data lake. Collaborate with cross-functional teams and Data Engineers to add features and maintain Go-based data pipelines. Improve platform architecture for scalability, observability, and reliability while sharing knowledge and mentoring teammates.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Design and operate end-to-end infrastructure for intracortical neural data, from ingestion and validation through preprocessing, versioned dataset releases, and training data loaders. Preserve scientific metadata and provenance while implementing signal processing, quality checks, storage optimization, parallel processing, and monitoring. Collaborate with neuroscience, machine learning, infrastructure, and IT teams to support scalable, reliable model training and sensitive research-data management.

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Build scalable data foundations, analytics tooling, analysis pipelines, and trusted self-service datasets for product and data science teams. The role focuses on instrumentation, experimentation standards, data quality, metric governance, documentation, workflow orchestration, and cross-functional collaboration. Engineers will use SQL, Python or R, and AI-assisted workflows while validating results and protecting sensitive data.

Leads client-facing delivery of large-scale data engineering, analytics, and data platform initiatives. Serves as the bridge between client stakeholders, executive leadership, and offshore teams while owning project execution, governance, solution alignment, risk management, and stakeholder satisfaction. Oversees data infrastructure, migration and cloud transformation engagements, engineering practices, technical teams, and escalation resolution.

Build and own Lightspark’s internal AI and data platform, including agent infrastructure, MCP servers, tool integrations, governed data, sandboxed execution, and production pipelines. Partner with Finance, Sales Ops, Compliance, and other teams to replace manual workflows with reliable systems. Extend dbt and cloud data warehouse infrastructure, support monitoring and data reliability, and improve engineering tooling. The role requires production experience with AI agents, LLM APIs, internal platforms, and TypeScript/Node.js or Python.