Top Data Engineer Jobs in Portugal

Designs and implements large-scale data solutions using Java and open-source big data technologies (Hadoop, NoSQL, Solr, Storm, Kafka). Participates in architecture and design discussions, delivers implementations, collaborates with senior architects and offshore teams, authors technical papers, and shares knowledge across the organization while ensuring performance, scalability, and timely quality delivery.

Design and implement large-scale data processing solutions (GB/PB) using Hadoop, NoSQL, MPP and related open-source tech. Participate in architecture/design, provide implementation details to offshore teams, produce whitepapers/case studies, ensure timely quality deliveries, and share knowledge through training sessions.

Work on client onboarding/migration as a Data Analyst/Database Developer: gather business requirements, perform data analysis, mapping, profiling and query optimization. Coordinate with US/UK/India teams and vendors, ensure data quality via control scripts, identify source systems and eliminate redundancies. Assist BUAT, train new resources, and support extracts, views, and client reports. Financial services domain expertise required.

Design, implement, and maintain enterprise database solutions and Big Data (Hadoop) systems. Lead database architecture, HA/DR, migrations, patching, performance tuning, security, capacity planning, monitoring, and incident recovery. Provide technical leadership, SOP documentation, and support BI/data mining activities. Role requires shift work.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Build and maintain Power BI dashboards and reports, partner with stakeholders to define KPIs, ensure data accuracy and governance, resolve reporting issues, work with Microsoft Fabric/Delta Lake, and lead small analytics efforts while mentoring others.

The role involves evaluating IT security data, documenting security gaps, developing interoperability measures, prototyping algorithms, and generating analytical reports. Strong network engineering knowledge and TS/SCI clearance are required.

The Senior Data Engineer will optimize the data platform, build scalable data pipelines, improve data access and quality, and establish data engineering discipline within the company.

As a Senior Software Engineer, you will design APIs, manage database architecture, optimize performance, and collaborate with cross-functional teams to support the Data Platform Team's backend solutions.

Lead design and implementation of scalable data infrastructure to ingest, transform, and serve structured and unstructured financial data. Build high-performance streaming and batch pipelines, integrate heterogeneous data sources, ensure security and data quality, enable AI consumption, and mentor engineering teams while driving observability and reliability.

The job involves requirements engineering for software systems related to data platforms, focusing on the analysis and gathering of system requirements.

Hands-on senior role to design, develop, and support AI, analytics, data engineering, IoT, and intelligent automation solutions. Build scalable data pipelines, API integrations, streaming and operational intelligence platforms, generative AI agents, predictive maintenance models, and Power BI reporting to improve operations, asset performance, and decision-making across the enterprise.

The Senior Data Engineer will architect and enhance data infrastructure, create dependable data pipelines, oversee cloud-based data warehouses, and mentor engineers to enable scalable solutions for analytics and machine learning.

Build, operate, and scale governed data pipelines and curated lakehouse datasets on Azure to support analytics, operational intelligence, and AI-enabled use cases. Deliver production-ready PySpark/SQL transformations, manage pipeline monitoring and performance, document lineage and business rules, and collaborate with stakeholders to ensure secure, governed, and reusable data products.

The role involves building large-scale data systems for robotics AI training, focusing on data pipelines, storage, and workflows.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Design and build scalable log ingestion pipelines, ensure system reliability, and optimize performance in a high-throughput environment.

This role involves designing and implementing large-scale big data platforms using Java and various big data frameworks, along with integrating systems and managing databases.

Lead strategy and operations for on-prem Kafka and support Perforce-based asset version control. Maintain mission-critical Linux services, automate with scripting and IaC, troubleshoot complex issues, coordinate with production/engineering/vendors, mentor teammates, and participate in on-call rotations.

The Big Data Engineer role requires strong hands-on experience with Big Data technologies, AWS services, and programming in Java or Scala.

Lead design and build scalable, secure data platforms and pipelines for AI/ML. Own ingestion, transformation, storage, quality, monitoring, and real-time/batch processing. Collaborate with data scientists and ML engineers, drive data modeling and MLOps best practices, ensure governance and mentor junior engineers.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

As a Data Platform Engineer/Platform Lead, you'll design, support, and optimize data platforms for digital products while collaborating with cross-functional teams and managing complex DevOps initiatives.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.