Top Data Engineer Jobs in Portugal

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.

Implement and deliver SaaS data products for investment banking clients: design and build scalable data pipelines and integrations, productionize solutions, provide L3 support, mentor junior engineers, and collaborate with product and client stakeholders to optimize adoption and architecture.

Hybrid hardware/software engineer responsible for characterizing 4D LiDAR sensors, supporting customer integrations, building production Python/C++ data pipelines, running calibration and field debugging, maintaining ETL for high-volume sensor data, and coordinating data labeling and collection operations.

Lead infrastructure strategy and build/automate AWS infrastructure. Maintain and grow data lakehouse (Databricks) and event processing. Champion security, CI/CD, monitoring, model serving, and provide engineering leadership while participating in L2 support rotation.

Own and build the data foundation for an AI sports-performance platform: design data architecture, real-time streaming ingestion, time-series storage, feature store and millisecond feature serving, sport knowledge graph and evaluation/calibration frameworks, ensure tenant-level isolation and provenance, and work closely with domain scientists and AI engineers to deliver trustworthy, production-grade ML infrastructure.

Design, build, and maintain cloud-based ETL pipelines and data warehouses (AWS Glue, Redshift, S3, Spark). Ensure data quality, collaborate with analysts/data scientists, document data models, monitor and troubleshoot pipelines, and adhere to federal data governance and compliance.

Design and operate metadata cataloging, lineage harvesting, governance workflows, and data quality for unstructured data. Configure and maintain Atlan/Anomalo/Unity Catalog, build automated checks and prototypes using Python/SQL/Databricks/Streamlit, collaborate with stakeholders, document standards, and drive governance tool adoption.

Scope, implement, validate, and monitor merchant data integrations to ensure accurate input to data science models. Maintain data configuration, create JSON/CSV templates, write Python/SQL/Spark/Databricks/R scripts for testing and transformation, upload historical data, triage alerts, and coordinate with internal and client teams to resolve data issues and improve pipelines.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

As a Senior Lead Data Engineer, you will lead teams to design technical solutions, focusing on data integration and AI capabilities to support business needs, applying your expertise in cloud computing and big data technologies.

Design, build, and maintain scalable data pipelines and cloud data infrastructure (warehouse, RBAC, validation). Collaborate with data scientists and engineers, orchestrate ML pipelines, ensure data quality/security, optimize cost and performance, lead incident response and mentor teammates.

Design, optimize, and operate large-scale data pipelines focused on performance and scalability. Support ML workflows including feature extraction, model training, and real-time inference. Build solutions using Python, SQL, AWS data services and Apache Spark, and train/deploy models with SageMaker, TensorFlow, or PyTorch in a fast-paced remote environment.

Design, build, and maintain reliable ELT/ETL pipelines and Snowflake data models using dbt, Dagster (or Airflow/Prefect) and Python. Ensure data quality, CI/CD deployment, documentation, stakeholder collaboration, and AI-ready data layers to support analytics and Snowflake Cortex use cases.

Responsible for building data models, developing ELT pipelines, implementing data quality checks, and utilizing best practices in data engineering and healthcare analytics.

Design, build, and maintain scalable data pipelines and architecture to support analytics and applications. Develop datasets and analytics tools, automate data workflows, ensure security/privacy of healthcare data, troubleshoot data issues, and partner with Product, Engineering, and business stakeholders to deliver data solutions.

Design, develop, and maintain databases and scalable data pipelines. Build stored procedures, triggers, and ETL processes; perform testing, tuning, security, and data quality work. Collaborate with developers and stakeholders, document architecture, review code, and support reporting and integrations to enable restaurant and customer-facing systems.

Design, build, deploy, and support high-performance data pipelines on GCP. Collaborate with business, IT, analysts, and data science teams to capture requirements, ensure data quality and lineage, optimize pipeline performance, document designs, and troubleshoot operational issues.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

Build and maintain Power BI reporting for FP&A: extract and transform data (Bronze/Silver), semantic modeling and dashboards (Gold), ensure accuracy, security, performance, and manage Power BI Service access and role-level security.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

The Data Engineer will design and implement data ecosystems, build data pipelines, and ensure data quality while collaborating with stakeholders and utilizing AWS services.

As an AI Data Engineer, you will build and maintain data pipelines and AI models to enhance GTM strategies, improving decision-making and operational efficiency.

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.