Top Data Engineer Jobs in Portugal

Leads the design and development of data engineering and AI-enabled solutions for legal, client, and internal business applications. Builds Python and SQL workflows for structured and unstructured data, integrates generative AI and APIs, validates AI outputs, establishes human-in-the-loop quality controls, and supports responsible AI governance. Collaborates with attorneys and technical teams, evaluates emerging technologies, prototypes self-service tools, and provides technical guidance to less experienced professionals.

Develop and maintain scalable data platforms, pipelines, ETL and ELT workflows for structured and unstructured data. Build software and scripts to retrieve, parse, process, and convert data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile teams while supporting platform monitoring, scaling, and operations for mission-driven client projects.

Designs, develops, tests, documents, and deploys software enhancements for Clinical Solutions products. The role includes front-end development, API and database work, troubleshooting, release preparation, peer reviews, and application support. It mentors developers, promotes engineering standards, coordinates issue resolution with business and IT teams, and contributes to agile planning. The engineer is also expected to apply AI-first development practices and communicate best-practice technical solutions.

Develop and maintain ETL pipelines supporting data ingestion, transformation, preparation, labeling, querying, and analytics. Troubleshoot data and system issues at scale, work with structured and unstructured data, and support government and cybersecurity data operations. Collaborate within an agile team using Linux, cloud, Kubernetes, and data-flow technologies while ensuring quality, testing, and timely delivery.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Lead the architecture and delivery of end-to-end Azure data platforms, including ingestion, transformation, modeling, analytics, activation, APIs, and agentic AI integrations. Build production-grade batch and streaming pipelines using Microsoft Fabric, Azure Databricks, PySpark, Python, and SQL. Establish governance, security, lineage, and performance standards; troubleshoot critical systems; mentor engineers; advise client stakeholders; contribute to presales and reusable engineering frameworks.

Build and maintain Azure-based ETL and ELT pipelines supporting a layered cloud data lakehouse. Integrate ERP and operational data through REST APIs and SQL sources, implement incremental loading and schema evolution, manage orchestration workflows, and monitor data quality and pipeline health. The role also includes data modeling, documentation, testing, deployment practices, code reviews, and collaboration with leadership while developing toward broader engineering ownership.

Build and operate large-scale data pipelines processing terabytes of security intelligence on AWS. Develop Java software, integrate data sources, improve rating algorithms, and evolve cloud data warehouse infrastructure. Collaborate with data science, research, engineering, design, and product teams to deliver reliable cybersecurity insights. The role emphasizes scalable software development, data quality, continuous improvement, and strong communication.

Build and maintain healthcare data ingestion pipelines and dbt transformation models in Snowflake. Contribute to fact and dimensional modeling, semantic-layer development, data quality troubleshooting, pipeline reliability, and governed reporting. Collaborate with engineers, analysts, and stakeholders to translate requirements into technical data structures. Use AI-assisted development tools while following testing, documentation, security, SDLC, and change-management practices.

Build GCP data pipelines, ETL/ELT processes, integrations, data models, and analytics solutions. Support AI/ML development with Vertex AI and Gemini, including MLOps, prompt engineering, embeddings, and RAG experimentation. Participate in client requirements gathering, technical implementation, data governance, quality, security, monitoring, and documentation while collaborating with senior engineers and architects.

Administer and optimize high-performance storage for Linux-based HPC systems, including parallel filesystems, SAN, NAS, archives, hardware, quotas, capacity, and performance. Develop automation scripts, troubleshoot infrastructure and storage issues, support maintenance and upgrades, apply security and configuration controls, and collaborate with HPC, systems, network, cybersecurity, and government stakeholders in a classified environment.

Leads development of scalable applications, data pipelines, machine learning models, inference services, APIs, and MLOps capabilities. Builds integrations across Salesforce, CRM, web, mobile, databases, and enterprise systems. Designs CI/CD, monitoring, validation, model versioning, and production support processes while improving reliability, security, scalability, and observability. Collaborates across product, data, architecture, business, and engineering teams to deliver machine learning solutions from requirements through continuous improvement.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.

Designs, develops, tests, deploys, and monitors scalable data pipelines using Azure Data Factory, Databricks, Python, SQL, and dbt. Integrates source systems with data warehouses and other platforms, automates data processes, improves infrastructure scalability, and supports data migrations during mergers and acquisitions. Ensures data security, privacy, retention, and effective enterprise data management.

Build and lead scalable data and machine learning systems for an AI platform automating product regulatory compliance. Responsibilities include designing system architecture, selecting technologies, developing backend and internal services, collaborating with data and ML engineers, and balancing speed with scalability. The role requires strong Python, database, microservices, AWS, and Docker expertise, along with startup product development experience and technical leadership.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines. Ingest, transform, and analyze structured and unstructured data supporting analytics, machine learning, and reporting. Develop reliable production systems using Spark, streaming technologies, SQL, orchestration tools, and distributed systems principles. Troubleshoot, document, and optimize data workloads for performance and cost. Preferred experience includes cloud Hadoop platforms, lakehouse formats, governance tools, Kubernetes, CI/CD, and infrastructure as code.

Define and lead enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Establish data modeling, security, lifecycle, and platform standards; architect lakehouse, warehouse, and streaming solutions; integrate governance and catalog tools; design scalable pipelines, disaster recovery, and high availability; optimize costs; guide architecture reviews; partner with ML, BI, and product teams; and mentor data engineers and architects.

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

Design and architect scalable healthcare data systems, core data models, and production data pipelines. Integrate and normalize healthcare data using TUVA, OMOP, and FHIR standards; establish data quality and governance; optimize performance; deploy secure cloud infrastructure; ensure regulatory compliance; and build bi-directional EMR integrations. The role also requires independent technical decision-making, test automation, CI/CD, and DevOps practices in a fast-paced healthcare technology startup.

Lead Data Privacy Engineer responsible for embedding privacy principles, ensuring regulatory compliance, and leading data protection initiatives across systems and cross-functional teams.

Lead design, deployment, and operational support for enterprise data protection across hybrid cloud and on-prem. Focus on encryption, tokenization, key management, HSM operations, integrations with apps and storage, automation via IaC, incident response, vendor strategy, and troubleshooting. Partner with application and infrastructure teams to ensure secure, stable production data protection and compliance.

Leads development and technical direction for a cloud-native Master Data Hub supporting trusted enterprise data. Designs applications, APIs, data models, integrations, pipelines, and automation; improves reliability, security, monitoring, and operational efficiency. Mentors developers, influences architecture, troubleshoots complex issues, manages releases, and partners with commercial, governance, regulatory, supply chain, finance, and technology teams to enable commercialization, analytics, AI, and digital commerce.

Build and operate AWS-based data platforms, Java/Spring Boot services, reliable data pipelines, and AI-powered applications. Responsibilities include data ingestion and modeling, predictive ML, generative AI and RAG with Bedrock and OpenSearch, production monitoring, security, CI/CD, infrastructure as code, and integrations with databases, POS, payment, and partner systems.

Designs and maintains scalable data pipelines, models, warehouses, lakehouses, and cloud-based data platforms. Builds ETL/ELT workflows, supports AI/ML data needs, implements data quality and governance controls, and manages batch and streaming integrations. Collaborates with technical and business teams to deliver reliable data solutions, troubleshoot infrastructure, and improve scalability, performance, and business value.