Top Data Engineer Jobs in Portugal

Design, build, and maintain scalable data pipelines and a customer-facing recommendation engine (including LLM logic). Manage data warehouse, data quality monitoring, CI/CD for pipelines, and partner with product, analytics, engineering, and marketing to ensure consistent metrics and observability.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.

Lead design, build, optimization, and operation of scalable Databricks-based batch and streaming data pipelines for healthcare programs. Ensure DataOps, data quality, lineage, observability, CI/CD, and compliance with CMS and federal healthcare regulations. Collaborate with architects, data scientists, and stakeholders, troubleshoot production issues, and mentor junior engineers.

Senior Staff Data Engineer designs, builds, and owns cloud-based ETL/ELT pipelines and data products for Sales and Underwriting. Responsibilities include solution architecture input, CI/CD implementation, data quality and replication, hybrid cloud integration (AWS/Azure), mentoring, and evaluating new data technologies to support analytics and operational decision-making.

Design, build, and operate large-scale data acquisition pipelines and backend services. Implement ETL/ELT for batch and streaming flows, develop Java production services, ensure schema evolution and data quality, optimize performance and cost, and provide observability. Collaborate with product, data science, and platform teams to translate requirements into production-ready systems.

Design, build, and optimize scalable end-to-end data pipelines and Snowflake data warehouses. Develop BI solutions (Power BI/Tableau/Cognos), enforce data quality/governance, integrate investment data vendors, collaborate with stakeholders, mentor junior engineers, and follow SDLC/DevOps best practices.

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.

Design, build, and operate production LLM-driven agents that discover and verify business web presence, link people to entities, score legitimacy/risk, and maintain shared agent infrastructure (tool registry, eval harness, instrumentation). Own model selection, prompt/tool engineering, eval methodology, and cost/latency optimization for enrichment pipelines.

Design, build, and operate reliable, scalable backend services and platform capabilities for monetization (pricing, billing, ads, payments). Own architecture, integrations, correctness, observability, and operational excellence. Lead cross-functional technical projects, write RFCs, mentor engineers, and deliver reusable developer tools and data products for product, finance, and GTM teams.

Build, test, and deploy scalable data platform services supporting ETL/ELT, streaming, and analytics. Collaborate with global teams, troubleshoot production issues (on-call), optimize performance, and maintain documentation and observability for platform reliability.

Design, build, test, and deploy scalable data platform services (ETL/ELT, streaming, batch) using cloud and distributed processing frameworks. Support production on-call, collaborate with global teams, improve performance, and maintain platform documentation and observability.

Lead design, development, and administration of enterprise Power BI reporting, semantic models, and Azure-based data pipelines. Deliver executive dashboards, KPIs, advanced analytics, and governance; mentor teams and modernize BI capabilities while ensuring performance, security, and data quality across the organization.

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

As a Data Engineer II, you will develop and manage data lakes and warehouses, monitor database performance, optimize queries, and automate tasks using infrastructure as code.

Design, build, test, and maintain production-ready, scalable API services and platform solutions using microservices and containerization. Collaborate with engineers, Data Engineers, CloudOps and Product to deploy and operate services on cloud platforms, ensure observability and readiness, and participate in Agile lifecycle activities.

Partner with business leaders to define data problems, process and analyze complex datasets, build predictive models and ML algorithms, perform EDA and A/B testing, create dashboards and visualizations, and work with data engineers to build reliable data pipelines to inform strategy.

Design, develop, and prototype compliance and business-processing analytics and ML/AI tools. Automate compliance functions, build and monitor machine learning models (including Random Forests and feature development), integrate and test algorithms/POCs, instrument metrics and monitoring, and produce and review technical documentation.

Design, deploy, and maintain scalable cloud data infrastructure on AWS and GCP. Build and automate data ingestion/processing frameworks, CI/CD pipelines, and cloud automations. Optimize and troubleshoot data pipelines and system performance, ensure security in transit and at rest, and collaborate with data engineering, data science, and security teams. Mentor peers and follow agile practices.

Design, build, test, and maintain ETL pipelines, data warehouse and BI solutions. Partner with business users, perform performance/regression testing, review code, troubleshoot data issues, and oversee production deployments.

Lead the development and operation of a data lake for cyber operations, design schemas and indexes, build and evolve ETL pipelines, mentor engineers, and drive technical initiatives.

The role involves leading the development of data lakes, designing schemas, building ETL pipelines, and mentoring engineers while ensuring data quality and performance.

The Senior Data Engineer will design and maintain data processing systems, enhance ETL components, and ensure data security and integrity while mentoring junior staff.

Develop and optimize Hadoop/Big Data solutions using Java MapReduce. Work with Hadoop ecosystem tools (Pig, Hive, HBase, Oozie), NoSQL databases (Cassandra/MongoDB), reporting tools (Tableau/Platfora), Unix environments, and optimize ETL jobs. Execute major projects and communicate effectively with stakeholders.

Design, develop, debug and tune Hadoop-based ETL using HDFS, Pig, Hive, HBase, Sqoop and Java. Provide technical oversight to the Hadoop team, write/modify shell or Python scripts, liaise with application teams, perform performance tuning and troubleshooting, and provide on-call weekend support.