Top Data Engineer Jobs in Portugal

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.

Lead architecture and delivery of scalable, secure enterprise data and AI integration solutions. Own integration strategy, API‑first and cloud‑native designs, data product onboarding, governance, observability, and migration to Azure/Databricks/Microsoft Fabric while guiding cross-functional teams and ensuring compliance, performance, and operational readiness.

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.

Design, build, and maintain scalable ETL pipelines and data warehouse architecture. Collaborate with stakeholders to deliver analytics-ready data, ensure data quality, governance, and compliance, automate data workflows, optimize performance, and document data systems for cross-functional teams.

Lead architecture, development, and operation of the company data and AI platform. Define technical strategy, build and mentor a high-performing data platform team, drive company-wide initiatives, deliver scalable multi-region data pipelines, oversee ETL frameworks, metrics stores, infrastructure and data security, and advocate best practices for lakehouse-style data processing and model governance.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

As a Software Engineer III at GlossGenius, you will lead data engineering efforts, designing and implementing data models and pipelines while mentoring engineers and shaping the data platform's vision.

Manage data requirement and electrical change requests for the U.S. Navy Strategic Weapon System. Coordinate with contractors, lead pre-coordination meetings, develop and drive change requests, perform impact reviews on SPALT and contractor documentation, support technical resolution with SSP working groups, and implement changes into SSP documentation. Limited off-site travel may be required.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.