Top Data Engineer Jobs in Portugal

Build large-scale data systems for frontier coding-model pretraining across data quality, data platform, or web crawling. Responsibilities include developing high-throughput pipelines, training and deploying data-quality models, orchestrating reproducible datasets, improving crawl coverage and parsing, and hardening distributed infrastructure. The role requires independent end-to-end ownership, performance optimization, observability, experimentation, and close collaboration with research, training, acquisition, and engineering teams.

Design and build cloud cost-management capabilities, including forecasting, anomaly alerts, cost allocation, data pipelines, transformation models, visualizations, and reusable UI components. Own features end to end from planning through release while improving application reliability and performance. Collaborate closely with engineers and founding team members, contribute to technical design and roadmap decisions, and work pragmatically in a fast-moving distributed startup environment.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

Lead modernization of data platforms, design and maintain ETL/ELT pipelines and data warehouse models, improve data governance and quality, optimize platform performance, and collaborate with IT, business units, and leadership to drive data-driven initiatives and stakeholder alignment.

Design and deliver scalable software, data engineering, and AI solutions for Barclays’ Liquid Financing business. Build and optimize cloud-based platforms using Python or Java, AWS, Kubernetes, event-driven architectures, and real-time data pipelines. Integrate LLM APIs, embeddings, and retrieval patterns into applications while collaborating with product managers, engineers, quants, and strats. Provide technical leadership, guide architecture and strategy, manage risk and controls, advise stakeholders, and mentor less experienced specialists.

Build data filtering and curation pipelines for VLM and classifier training sets, develop model-based data-quality signals, design clinical evaluation methodology for radiology report generation, maintain clinical benchmarks, validate model-based evaluators against radiologist judgment, and implement continuous evaluation and regression testing across the research stack.

Design, build, and maintain cloud data pipelines and governance on Databricks or Snowflake. Optimize Databricks clusters, implement Delta Lake, automate CI/CD with Azure DevOps, integrate with Azure services, and create monitoring and governance solutions.

Design, build, and validate cloud data infrastructure and high-throughput pipelines for multimodal sensor (LiDAR, camera, GNSS/IMU) data. Ensure data quality, lineage, versioning, automated validation, IaC, and production stability while partnering with SW/ML teams and labeling vendors.

Design, implement, and maintain Azure-based ELT/ETL pipelines and data architecture. Migrate and normalize source data into ADLS, enforce quality controls, source control, documentation, and self-service analytics. Optimize storage/processing, audit pipelines, and explore AI-assisted tooling.

Lead design and operation of a cloud-native data platform: set technical direction, build scalable ingestion/processing/warehousing pipelines (batch & streaming), support ML data needs, mentor engineers, drive projects end-to-end, run planning/standups/on-call, and eventually lead a data engineering team.

Own and scale the data platform: design and implement ETL/ELT pipelines, optimize warehouse/lake architecture, support streaming and batch ingestion, enable ML workflows, mentor engineers, write design documents, participate in sprints and on-call rotation, and deliver reliable clinical data at scale to customers.

Design, build, and maintain core data platforms, services, and APIs that enable operational data access. Develop scalable backend systems, data pipelines, and automation; collaborate across teams; own services end-to-end from architecture through production support.

Build, operate, and scale distributed data infrastructure (Kafka, Spark, Flink, Trino, HDFS) to enable real-time ML pipelines and analytics at petabyte scale. Design high-throughput, low-latency ingestion and transport, optimize performance, debug distributed systems, and collaborate with ML and product teams to ensure reliable, production-grade data movement and compute.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Design and implement scalable data ingestion, ETL/ELT pipelines and transformations into Microsoft Fabric using ADF and notebooks (Python/Pandas/Spark). Build and maintain dimensional data warehouse models, ensure data quality, performance tuning, and prepare data for semantic models. Collaborate with stakeholders, participate in code reviews, documentation, and operational handoffs while establishing standards for deployment and governance.

Design, operate, and maintain flight test data systems for structural loads measurement; develop and validate loads equations, process large flight datasets, build automated pipelines, produce fatigue and loads spectra, compare test data to models, support stress and damage tolerance analysis, and compile certification deliverables while guiding instrumentation strategy.

The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.

The Voice/Data Engineer conducts installation, maintenance, and troubleshooting of telephony and data networks, focusing on technology connections and infrastructure performance.

The Voice Data Engineer will monitor and troubleshoot voice and data networks, coordinate telecommunications equipment installations, manage incidents with providers, and implement procedures for telecommunications systems.

Own and operate the unified data platform and data lake powering fraud detection. Design, build, and run low-latency batch and streaming ingestion pipelines, ensure data quality/lineage/observability, support embedding/vector pipelines for generative AI, and manage deployment and CI/CD on Kubernetes while partnering with data science and product teams.

The Sr. Data Engineer is responsible for database design, development, performance monitoring, and data analytics to support business needs and improve data management.

The Sr. Data Engineer is responsible for application development, database performance monitoring, data analytics, and ensuring compliance with quality standards.

The role involves database application development, performance monitoring, data analytics, and ensuring compliance with quality standards. The engineer will develop data architecture and mentor peers on data management best practices.