Top Data Engineer Jobs in Portugal

Design, develop, test, maintain, and support secure, scalable data solutions using Snowflake and Databricks. Lead technical design, author specifications, review code, mentor engineers, ensure SDLC, audit/security compliance, and drive technical strategy while collaborating with architects and business partners. Participate in Agile ceremonies, be on-call as needed, and travel occasionally.

Design, build, and maintain scalable ETL/ELT pipelines ingesting travel-related data from multiple sources. Integrate and model data in cloud warehouses (Snowflake/BigQuery/Redshift), implement data quality and monitoring, support real-time streaming, and ensure data governance and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, and optimization.

Design, build, and maintain ETL/ELT pipelines and enterprise data products. Monitor data pipelines, support data modeling and semantic layers, enforce governance, and collaborate with analytics and AI teams to deliver context windows and embeddings for AI/ML workflows.

Own end-to-end extraction and normalization pipelines that convert messy supplier emails and documents into a reliable, queryable data layer. Build entity-resolution, ML/NLP pipelines, agent workflows, evaluation harnesses, and infrastructure for data quality, lineage, monitoring, and model-driven extraction improvements.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

Build and own a versioned, documented gold data layer from silver Lakehouse sources for AI research. Transform, validate, and curate multi-modal datasets, build reusable Databricks/PySpark pipelines, automate quality and labeling, support researcher workflows, and maintain lineage and dataset snapshots for model development and evaluation.

Support AFSOC data integration and modernization by building and optimizing ETL pipelines, integrating diverse data sources, managing NoSQL and big-data infrastructures, configuring analytics frameworks, administering cloud and CI/CD tooling, and coordinating with stakeholders to enable downstream analytics and machine learning.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

The Data Engineer designs and maintains scalable data products, implements quality checks, and supports analytics and reporting needs, collaborating closely with business stakeholders to ensure data reliability.

As a Forward Deployed Data Engineer, you will build data management platforms for maritime awareness, implementing data pipelines, models, and improving workflow efficiency. You'll work on-site, writing code and providing technical solutions, ensuring high-performance results.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

As a Data Engineer, you will design financial knowledge graphs, create graph processing pipelines, and implement best practices for knowledge graph development.

Build and own customer data ingestion and large-scale pipelines powering AI workers. Design syncing, retrieval, and data lake systems, improve reliability/observability, partner with product/backend teams, and contribute across infrastructure to give AI agents access to enriched customer data.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

The Data Engineer will design, implement, and maintain data pipelines, collaborate with cross-functional teams, ensure data integrity and optimize solutions to support analytics and reporting.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest third-party APIs, optimize columnar formats and query performance, and consult with stakeholders to translate business goals into data architecture roadmaps using Spark, Snowflake, and cloud infrastructure.

Design, build, and maintain scalable AWS-based data pipelines, data lakes and warehouses. Implement data modeling and warehousing for Redshift/Athena, ensure data quality, optimize queries, and develop production-grade Amazon QuickSight dashboards with security (RLS), performance tuning, and stakeholder-driven reporting.

Design, build, and operate high-performance inference platforms for serving large ML models. Focus on request routing, batching, caching, autoscaling, GPU utilization, observability, performance engineering, and production reliability for diverse model workloads.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Ingest, transform, and analyze structured and unstructured data to support enterprise analytics, ML, and reporting. Ensure reliable, performant production data platforms and troubleshoot, document, and optimize workflows.