Top Data Engineer Jobs in Portugal

Own and build Grow Therapy's data security infrastructure: automated data classification, field-level masking/tokenization, encryption and key management, secure data connectors to AI tooling, and access controls. Define a multi-year vision, implement pipelines and services, and partner across Data, Engineering, and Detection & Response to make secure-by-default the company standard.

Lead design and delivery of Klaviyo's scalable data platform (streaming, batch, lakehouse, governance). Define SLOs, author ADRs/RFCs, implement ingestion/CDC, processing, storage, catalog/lineage and access controls. Optimize performance/cost, apply ML/GenAI to data workflows, partner with product/analytics, contribute code/reviews, and mentor senior engineers to enable reliable, multi‑tenant data at scale.

As a Senior Developer Advocate, you will lead community engagement in Data Observability, create content, mentor teams, and enhance product feedback from engineers.

Lead discrete-event simulation modeling of vehicle production systems using C-Draw2 and Siemens Plant Simulation to identify bottlenecks and optimize throughput. Manage model conversion, develop custom library objects and reports, validate performance datasets, mentor a team of simulation engineers, and collaborate with manufacturing stakeholders to implement cost-effective automation and material flow improvements. Support digital twin initiatives and provide leadership-level reporting to Manufacturing Leadership.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data publication and transformation solutions. Collaborate across Agile teams to build distributed, real-time data pipelines and data warehousing (Redshift/Snowflake), perform unit tests and code reviews, mentor engineers, and adopt emerging technologies to deliver scalable data platforms.

Design, build, and maintain scalable Spark/Python data pipelines and automated workflows. Enforce data governance, quality, lineage, and observability. Support ML pipelines (feature engineering, validation, monitoring) and document data assets, contracts, and standards.

The Lead Data Engineer will design, implement, and maintain data pipelines for mission-relevant data, ensuring compliance with security frameworks and optimizing data accessibility and interoperability across mission systems.

Operate and evolve AMP platform to ensure reliability, security, compliance, cost efficiency, and operational excellence. Monitor health and capacity, manage incidents and escalations, provision Azure/Fabric resources, enable onboarding, drive security and compliance remediation, optimize costs and capacity, coordinate with product groups and vendors, and improve automation, observability, and operational tooling.

Develop, optimize, and support ETL/data integration solutions (SSIS) to automate data delivery. Collaborate with analysts, senior developers, and architects; monitor executions, troubleshoot data issues, and follow established SDLC practices. Hybrid work with periodic on-site presence.

Design, build, and maintain scalable ETL pipelines and a cloud-based data platform. Create dashboards, troubleshoot data discrepancies, implement testing/CI/CD/IaC improvements, and collaborate with stakeholders, software engineers, and data scientists to deliver reliable BI solutions.

Build and maintain scalable data pipelines, warehousing, and analytics tooling to support AI safety monitoring, abuse detection, and enforcement. Integrate multiple data sources, implement data quality and governance, create dashboards and self-service tools, and partner with research and policy teams to inform safety interventions.

Design, build, and own scalable ETL pipelines, dashboards, and data quality processes. Troubleshoot discrepancies, expand the cloud data platform, implement CI/CD and IaC practices, and collaborate with stakeholders, developers, and data scientists to deliver BI solutions.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Build and operate large-scale data pipelines (batch and streaming) for multi-terabyte audio/video datasets. Deploy and scale containerized workloads on Kubernetes/AWS, maintain Spark/Ray jobs, run workflow orchestration (Airflow), tune enterprise databases, and collaborate with ML teams on training and MLOps workflows.

Build and maintain scalable ETL/ELT data pipelines, write production Python and Node.js code, develop data models, schedule and monitor workflows, optimize performance, ensure data quality, troubleshoot issues, collaborate with analysts and product teams, and document solutions.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Build and maintain data pipelines, applications, workflows, and governance within Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot, MRO, and supply chain processes, integrate ERP/MRO data into models, run capacity/throughput analyses, deliver decision‑support briefings, and support adoption of advanced analytics and digital engineering. Coordinate with stakeholders and support business development activities.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Design, build, and deploy data pipelines, databases, and AI-enabled automation to improve manufacturing test throughput, yield, and reliability. Integrate test systems and IoT sensors, support root-cause analysis, develop dashboards and alerts, and work hands-on with production teams for on-floor troubleshooting and sensor/validation tasks.

Build, maintain, and optimize data pipelines and ETL to ingest structured and unstructured sources. Configure NoSQL/SQL and big-data frameworks (Spark, Hadoop, Kafka, Hive, etc.), administer cloud and CI/CD environments, apply distributed-systems principles, create APIs and performant queries, and coordinate with stakeholders to support analytics and downstream ML work.

Design, develop, and maintain .NET console applications that integrate databases and perform ETL for compensation and talent management systems. Manage full SDLC, optimize SQL (stored procedures/queries), build data interfaces, collaborate with stakeholders, and evaluate emerging technologies to support a federal client on-site.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark/Spark SQL), enable Power BI datasets, implement CI/CD and IaC, ensure data governance/quality with Purview, support cloud migrations and production operations while collaborating with stakeholders and agile teams.

Build and maintain scalable, secure data pipelines and AI-ready datasets using Azure Databricks, Snowflake, Python, and Spark; support RAG solutions, integrate Azure AI services, implement data governance, CI/CD, and production monitoring for enterprise generative AI initiatives.