Top Data Engineer Jobs in Portugal

Design, develop, test, document, and support secure external data integrations (APIs, file-based interfaces, ETL) using .NET, Azure, Informatica, and Snowflake. Lead technical delivery, perform impact analysis, provide production support, mentor engineers, and ensure solutions meet security, performance, and governance requirements.

Lead architecture, operation, and improvement of cloud data and analytics platforms (Fabric, Snowflake, Databricks). Own CI/CD for data assets, BI delivery (Power BI/Tableau), reliability, monitoring, incident response, security, and governance. Enable teams through standards, runbooks, and onboarding; troubleshoot performance and ensure production stability.

Design, build, and maintain scalable data pipelines ingesting TBs/day of security telemetry; integrate APIs and event streams; normalize and enrich data; build entity-correlation and search capabilities; collaborate with detection engineers and product teams; set data engineering standards, observability, and testing.

Build and maintain data ingestion pipelines and platform components (Azure, Databricks, ADF, ADLS). Support SQL Server environments, data modeling, curated datasets, semantic models, and dashboard enablement. Contribute to data governance, monitoring, metadata, and cross-team incident response. Provide operational support and assist adoption of AI/automation initiatives.

Design and build backend and data systems for autonomous labs: scheduling (Temporal workflows), digital-twin modeling, data pipelines (Flyte), S3 lakehouse and PostgreSQL modeling, capacity planning services, and operate services on AWS EKS with Docker, Terraform, and CI/CD for scale and reliability.

Lead reliability, scalability, security and performance for enterprise cloud data platforms (Databricks, Azure, Cognite). Drive platform modernization, automation, observability, IaC/CI-CD, governance, and SRE practices. Mentor engineers, define standards, manage KPIs/SLOs, and partner with architecture, security, FinOps and business teams to support mission-critical analytics and AI workloads.

The Senior Data Engineer is responsible for building AI-driven healthcare automation systems, managing data workflows, and ensuring accurate reporting, particularly in healthcare claims processes.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Design and implement AI-optimized data platforms and production ETL/ELT pipelines, build semantic and analytical layers, write production-ready SQL/Python code, collaborate with stakeholders and analytics teams, and contribute to the firms data engineering practice and client delivery.

Lead and deliver AI-optimized data engineering solutions: design cloud data platforms, build ELT/ETL pipelines, implement semantic/analytic layers, write production SQL/Python/Spark code, apply AI-assisted tooling, and lead small teams on client engagements.

Lead end-to-end product engineering for data platform experiences, owning design and delivery of frontend and backend services. Build performant, secure UIs across desktop and web, integrate identity and access workflows, collaborate with cross-functional teams, and ship reliable, accessible, well-tested features.

Design, build, and maintain interactive Power BI and MicroStrategy dashboards supporting IT operations. Perform ETL and data integration, query complex databases, validate data accuracy, ensure DoW compliance (STIGs, IL5/6/7), collaborate with stakeholders, document standards, and train users to translate data into actionable insights.

Build and maintain Geotab's internal machine learning and generative AI platform. Implement logging, monitoring, and alerting; enrich big data with telematics at scale; support data integration and infrastructure for data scientists; document architecture and processes; assist internal teams with AI platform adoption and production deployments.

Build, operate, and modernize a cloud-native enterprise data platform powering SaaS AI products. Responsibilities include data ingestion, storage, querying, quality, governance, and operational excellence, collaborating with engineers to deliver scalable, maintainable solutions and refactor legacy systems.

Design, build, migrate, and maintain scalable data pipelines and architectures on Azure and Microsoft Fabric. Implement ETL/data integration, semantic models for analytics, CI/CD, monitoring, security controls, cost optimization, and testing. Collaborate with cross-functional teams and clients to translate business requirements into reliable, compliant data solutions.

Lead technical pre-sales engagements for Microsoft cloud database and analytics products. Deliver PoCs, demos, hackathons, and architecture workshops; design secure, scalable solutions; resolve technical blockers; collaborate with engineering; and evangelize Fabric, Databricks, Purview and Azure database technologies to accelerate customer migrations and adoption.

Design, build, and operate high-throughput real-time and batch data pipelines and analytics systems. Prototype and deploy data features, ensure data consistency, observability, and performance, and drive architecture and ownership while mentoring engineers and collaborating with product and infrastructure teams.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support analytics, ML, and reporting. Ensure reliable, performant production systems and troubleshoot, document, and optimize data workloads.

Lead architecture and engineering for enterprise healthcare data platforms. Design and implement scalable data warehouses, lakehouses, ETL/streaming pipelines, data governance/security, APIs, and infrastructure as code. Advise stakeholders, mentor teams, optimize performance and costs, and ensure compliance with clinical data standards (FHIR, HL7, OMOP) while delivering reliable data products for internal and external users.

Build and maintain governed, activation-ready marketing data products in Snowflake. Design canonical marketing data models, event-streaming, identity resolution, quality checks, and reverse-ETL activation for marketing, CRM, paid media and AI use-cases. Enable predictive scoring, attribution, and AI-ready context layers while partnering with cross-functional stakeholders.

Design and implement data integration and data warehouse solutions using big data technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and machine learning knowledge, and implement real-time streaming and aggregation patterns.

Lead technical ownership of the SQL Server data warehouse, replication architecture, ETL/ELT pipelines, and Power BI analytics. Design scalable relational models, optimize SQL Server performance, build and maintain semantic models and reports, establish data governance, and translate business needs into reliable, actionable reporting solutions across departments.

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.

Build and integrate deep learning models into software pipelines, collaborate with product and engineering teams, support data ETLs, and deploy solutions to cloud-hosted environments.

Provide technical service and support for AI data center systems, including installation, maintenance, troubleshooting hardware and software, and collaborating with clients to ensure high-performance enterprise computing operations.