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.

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.

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.

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

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.

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

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

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.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

Design, build, and operate scalable, secure data infrastructure including access control, financial reporting pipelines, cloud storage reliability, and data platform tooling. Collaborate with data scientists, analysts, and stakeholders to ensure data integrity, performance, cost-efficiency, compliance, and rapid recovery at petabyte scale.

The Technical Program Analyst will develop and automate applications using Microsoft Power Platform, focusing on ETL workflows and stakeholder collaboration for technical solutions.

Early-career Data & AI Engineer supporting client work and applied R&D. Prepare, clean, and validate heterogeneous data; build reproducible Python/SQL workflows; support ML and generative-AI tasks (embeddings, RAG, evaluation); prototype tools; document assumptions; participate in code review and translate project lessons into reusable assets.

Build and maintain scalable cloud-native data platforms and pipelines for analytics. Design system architecture, ingest high-volume bounded and unbounded data, optimize performance and cost, write tests and documentation, collaborate with product and EHR specialists, and adopt AI-assisted development tools to improve delivery and quality.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Lead design and implementation of scalable Azure-based data pipelines and architectures using ADF, Synapse, Spark/Databricks, and Power BI. Mentor engineering teams, ensure data security/compliance, collaborate with stakeholders, and drive adoption of best practices and new technologies.

Design, build, and maintain scalable ingestion, normalization, validation, storage, and lifecycle management of large-scale market and alternative datasets. Develop Python- and SQL-based pipelines, monitoring, and tooling to ensure research-ready, high-quality data across multiple asset classes while collaborating with researchers and infrastructure teams.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.