Top Data Engineer Jobs in Portugal

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

Lead hands-on data engineering and architecture for a classified on-prem Analytics Core: build ETL/ELT pipelines, clean and model 30 years of multi-modal and geospatial data, integrate ESRI spatial data, establish AI/ML feature standards, enforce DoD-aligned governance, and mentor transitioning staff to operationalize the solution.

Design, build, deploy, and support data integration and ETL pipelines on a Spark/Databricks and AWS-based platform. Lead requirements gathering, author technical designs and test plans, implement BI/analytics solutions, follow DevOps and agile practices, and improve integration architecture and operational procedures.

Design, build, operationalize, and maintain enterprise-scale data applications and scalable ETL/ELT pipelines. Develop and troubleshoot Palantir Foundry transformations, ontology objects, and logs. Collaborate with cross-functional teams to convert structured and unstructured data for analytics, monitoring, and platform operations.

Onsite Python data engineer supporting SOCOM in a classified SCIF. Build and maintain data pipelines, work with relational databases and SQL, develop on Mac/Windows/Linux, collaborate in agile teams, and support mission-focused data and analytics solutions.

Design, build, and maintain ETL/ELT data pipelines to ingest, transform, harmonize, and validate complex biomedical datasets. Collaborate with researchers and engineers to create analysis-ready, governed data products, dashboards, and APIs. Ensure data quality, lineage, security, documentation, and scalability while troubleshooting pipeline issues and contributing to platform modernization.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

Build and maintain software integrations and backend services to collect, normalize, and expose CPU and storage infrastructure data from heterogeneous sources. Design ingestion pipelines, data models, reconciliation and validation mechanisms, and interfaces to support analytics, planning, and operations. Partner with infrastructure teams to ensure reliability, observability, and scalability of data integrations.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Design, develop, and maintain SSRS reports and Power BI dashboards; perform data modeling and DAX calculations; support ETL (SSIS) and validate data across EMR/billing/CRM systems; gather and translate stakeholder requirements; liaise with offshore teams; perform testing, documentation, and ensure HIPAA/PHI compliance and data governance.

Lead technical strategy, architecture, and modernization of enterprise master data and data lakehouse platforms. Design and implement large-scale data pipelines, databases, and distributed systems using Python, Spark, Redshift, Iceberg and AWS services. Ensure performance, reliability, governance, CI/CD, and AI-enabled engineering practices while mentoring senior engineers and aligning cross-functional stakeholders.

Design, build, and maintain enterprise data warehouse and medallion-layer dbt Core models. Integrate MDM workflows, develop scalable pipelines using Microsoft Fabric/Azure Data Factory, optimize Snowflake performance, ensure data quality, support CI/CD and monitoring, troubleshoot production issues, and collaborate with cross-functional stakeholders to deliver analytics-ready datasets.

Design, develop, and support the Data Warehouse and reporting systems. Build and optimize SSRS reports, Power BI dashboards, and SQL ETL/queries. Ensure data accuracy, maintain documentation, and mentor team members while collaborating with business units to translate requirements into scalable data solutions.

Builds and operates scalable batch and real-time data pipelines (Databricks/Spark, Kafka), maintains Customer 360 data model and feature pipelines, ensures data quality/governance, delivers governed customer attributes to activation platforms, and supports privacy/compliance.

Design, build, and maintain ingestion and transformation pipelines into Salesforce Data Cloud from revenue systems; develop data models and identity resolution; integrate enrichment providers and Databricks; ensure data quality, lineage, and governance; troubleshoot, monitor, and document pipelines and integrations to support analytics and AI (Agentforce).

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and operate low-latency, highly available distributed data systems and services to deliver high-quality data for algorithms and ML. Partner with data scientists and product teams to enable self-service data, real-time and batch processing, observability, and scalable multi-tenant solutions that run 24x7.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Build and scale web‑scale data pipelines and ML systems for Netflix's Knowledge Graph. Process structured and unstructured data, apply information extraction and graph ML, ensure data quality, and collaborate with ML scientists and business partners to deliver productionized data and ML products.

Build and scale robust production data pipelines and automate complex game-related datasets, ensure high data quality, design schemas and data models, collaborate with logging infrastructure and analytics stakeholders to enable analysis across Netflix's games library.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Design, build, and operate end-to-end ETL/ELT pipelines to ingest network telemetry into a Data Mart. Create high-performance dimensional models, implement automated data quality and observability, enforce governance and IAM, maintain metadata and lineage, and enable analysts with production-ready datasets and standardized metrics.

Design, build, and maintain scalable end-to-end data ingestion and curation pipelines on GCP. Ensure data quality, governance, security, and cost optimization. Use Python, SQL, dbt/Dataform, Astronomer, and Terraform to automate workflows and infrastructure. Collaborate with architects and stakeholders, document processes, and translate business requirements into production-ready data assets for analytics and AI consumption.

Design, build and maintain data warehousing and ETL solutions (Informatica IICS, Teradata, Oracle PL/SQL, MS SQL). Create CI/CD pipelines, automate testing and monitoring, operate in agile/XP teams, mentor peers, design data pipelines on GCP/BigQuery/Airflow/Terraform/GitHub, and support development, validation and deployment of AI/ML applications.