Top Data Engineer Jobs in Portugal

As a Software Engineer in EHR Integrations, you will design, develop, and maintain software for EHR integration, clinical workflows, and data governance while collaborating with product managers and security teams.

Lead product development and optimization of internal databases and MES for battery R&D and pilot manufacturing. Define product roadmaps, write software requirements and user stories, oversee third-party developers, design UI/data visualizations, architect scalable database structures, maintain data integrity, and serve as primary contact for training, troubleshooting, and documentation across engineering and manufacturing teams.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to ingest, transform, and prepare data for analytics and ML, implement DevOps best practices, write tests and documentation, and improve data reliability, performance, and security using modern cloud and big-data technologies.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Lead end-to-end analytics solutions: gather requirements with stakeholders, build production-quality ETL and visualizations, coach junior analysts, communicate progress and roadblocks, support proposals and pitches, and contribute to firm initiatives and client delivery.

Lead design, build, and operate scalable, low-latency data systems and platform services for real-time personalization and recommendations. Partner with ML, product, and platform teams to deliver feature serving, inference integration, APIs, monitoring, and reliability. Provide architecture leadership, mentor engineers, and establish operational standards for data pipelines and ML workflows.

Lead design and implementation of scalable data architectures and pipelines for real-time and batch processing; mentor junior engineers; ensure pipeline reliability, monitoring, security, and governance; collaborate with cross-functional teams and adopt new data technologies.

Lead the data engineering team to design, build, and scale data platforms and pipelines. Set standards, manage projects and technical debt, collaborate with stakeholders, drive adoption of new tools, ensure data governance, and provide technical leadership for complex data issues and real-time architectures.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Lead engineer responsible for designing, developing, and optimizing petabyte-scale distributed data processing systems (Hadoop/Spark/Hive/Trino) on AWS. Leads performance tuning, system architecture, DevOps/CI-CD, security, AI tool adoption, and mentors engineers to improve product quality and delivery.

Lead and deliver complex data science and mathematical projects for DoD clients: design ML and statistical models, develop algorithms, perform EDA, integrate solutions, create dashboards and reports, mentor staff, and ensure compliance with DoD standards while driving research and innovation.

Lead development, tuning, and optimization of incrementality and attribution models for podcast advertising. Build, test, and refine statistical models on large datasets, collaborate with product and data teams, translate findings into actionable insights, and improve measurement methodologies to quantify ad impact.

Build and maintain a near real-time financial dashboard by integrating market and forecast APIs, automating financial models, and crafting clear visual narratives using Flourish and Canva. Collaborate with design and marketing partners, ensure data accuracy and performance, and optimize data flows across multiple feeds.

Build and maintain data pipelines and AI-enabled workflows using Python, SQL, and LLM frameworks. Implement vector search, embeddings, RAG, and prompt engineering to automate data classification, extraction, and reporting. Collaborate with analysts, engineers, and compliance teams to ensure accuracy, transparency, and regulatory adherence.

Design and build an end-to-end, high-throughput dataloading stack for massive multimodal datasets: formatting, preprocessing, filtering, sharding, caching, and streaming data to distributed GPU training with observability, reliability, and performance benchmarking.

Design, build, and maintain a production data platform for research: ingestion, ETL/ELT, object stores, SQL and NoSQL schema design, indexing, query optimization, observability, and high-throughput data movement. Collaborate with researchers, ML engineers, and infra to produce reliable interfaces for analytics, model training, and long-term archival while optimizing performance and maintainability.

Support cyber security compliance by analyzing vulnerability, authorization, and configuration data; transform and import data for visualization tools; produce metrics and graphical reports for IA compliance; maintain Excel-based visualizations; collaborate to simplify and automate the Heat Map.

Support cyber security operations by compiling reporting metrics, validating network/system security, assessing vulnerabilities, producing threat analyses and mitigation recommendations, participating in design reviews and cyber defense groups, and preparing briefings and documentation for senior US government intelligence and operations officials.

Design, build, and maintain scalable ELT/ETL data pipelines and data models on cloud platforms (preferably Snowflake). Enable batch and near-real-time integrations, ensure data quality/monitoring, create APIs and production-ready datasets for BI, analytics, and ML, optimize performance and cost, document architectures, evaluate new technologies, and mentor engineers.

The Data Systems Engineer will support data integration, pipeline development, metadata tagging, and data governance in a fast-paced environment, collaborating with technical and business teams to ensure data quality and system interoperability.

The DevOps Engineer will design, build, and optimize ML and data infrastructure on GCP, mentor a team, and ensure system reliability and performance.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The Senior Data Engineer will architect event-driven data pipelines, automate ETL processes, and ensure operational excellence in NVIDIA's financial infrastructure.

The Lead Data Platform Engineer will design and implement scalable data platforms, maintain data solutions, mentor junior engineers, and collaborate across teams.