Top Data Engineer Jobs in Portugal

Lead mechanical design for data center HVAC and heat-rejection systems, perform heat-load calculations and CFD airflow analysis, select cooling architectures, ensure redundancy and fault tolerance, coordinate with electrical/mechanical teams, and implement energy-efficient, air-quality, and sustainability strategies for scalable, mission-critical facilities.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Senior Mechanical Engineer for data center projects: design heat rejection and HVAC systems, perform heat load calculations, select cooling systems, run CFD airflow analyses, ensure redundancy and energy efficiency, coordinate with electrical/mechanical trades, and support capacity planning and air quality/filtration strategies for mission-critical facilities.

Design, build, and maintain scalable ELT/ETL data pipelines and data models on cloud platforms (preferably Snowflake). Enable batch and near-real-time integrations, ensure data quality/monitoring, create APIs and production-ready datasets for BI, analytics, and ML, optimize performance and cost, document architectures, evaluate new technologies, and mentor engineers.

Lead design, development, implementation, and maintenance of EDI solutions and ERP integrations. Manage trading partner onboarding, monitoring, mapping, automation (Workato), testing, vendor relationships, security/compliance procedures, and provide technical leadership and after-hours support.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

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.

Maintain and improve data platform reliability by automating pipelines, scheduling and CI/CD, implementing monitoring and observability, managing incidents and root cause analysis, and overseeing releases, deployments, rollbacks and performance tuning.

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.