Top Data Engineer Jobs in Portugal

Design, build, and operate large-scale batch and real-time data pipelines. Implement ingestion, transformation, orchestration, streaming, and downstream delivery using Kafka, Spark, cloud platforms, and workflow tools. Ensure reliability, scalability, performance, security, secrets management, and observability while collaborating with Agile teams to support enterprise analytics workloads.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Serve as on-site EHS technical resource for a data center campus: develop and maintain compliance programs, perform JHAs and risk assessments, manage hazardous waste (RCRA), lead incident investigations and corrective actions, deliver EHS training, maintain OSHA recordkeeping, inspect safe work practices, and advise operations and construction teams.

Partner with content teams to design and build analyses, metrics, dashboards, and data tools that measure and improve content performance. Develop scalable data pipelines and workflows, apply statistical and machine learning techniques for insight generation, and translate complex analytics into actionable recommendations for cross-functional stakeholders.

Design, implement, and own Oracle data architecture and schemas for a cloud-native centralized data platform. Develop PL/SQL/SQL artifacts, optimize performance, enforce data standards, support ETL/CDC pipelines, ensure secure scalable database solutions, and collaborate with cross-functional teams to deliver mission-ready data products.

Lead mechanical design for data center HVAC and heat rejection systems, perform CFD airflow analysis, select and size cooling equipment, ensure redundancy and capacity planning, optimize for energy efficiency and filtration, and coordinate mechanical/electrical integration on mission-critical projects.

Senior Mechanical Engineer responsible for designing data center HVAC and heat rejection systems, selecting cooling technologies, performing CFD airflow analysis, planning capacity and redundancy, optimizing energy efficiency and filtration, and coordinating mechanical and electrical integration across projects with occasional travel for client and onsite activities.

Lead mechanical design for data center heat rejection and HVAC systems, perform CFD airflow analysis, size cooling and redundancy, ensure energy efficiency, coordinate with electrical/mechanical trades, and plan capacity for phased growth and maintainability.

Design and optimize data center HVAC and heat-rejection systems, perform CFD airflow analysis, plan capacity and redundancy, coordinate with electrical/mechanical trades, and implement energy‑efficiency and air‑quality strategies for mission‑critical facilities.

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.

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 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.

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.