Top Data Engineer Jobs in Portugal

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Design and maintain data pipelines, ETL workflows, and graph database models. Build RAG and GraphRAG pipelines for generative AI applications using graph and data platforms. Collaborate with data engineers and scientists, engage client stakeholders, gather requirements, present progress, and translate business needs into technical solutions within a cross-functional consulting team.

Maintains physical servers, network devices, cabling, racks, and IT infrastructure across colocation data centers. Responsibilities include installations, hardware troubleshooting, vendor RMAs, inventory and shipments, documentation, rack optimization, and coordination with colocation and remote-hands teams. The role also supports office IT infrastructure, requires local travel between facilities, occasional flexible hours, and the ability to lift up to 60 pounds.

Serve as first point of contact for customers using Agentforce and Data 360, troubleshoot technical issues, provide feature guidance and best practices, manage customer expectations, create and maintain knowledge content, participate in training, and escalate complex issues to internal teams to ensure customer success.

Build and maintain automated data pipelines, ETL/ELT workflows, integrations, migration processes, and CI/CD automation for USDA’s HCM modernization initiative. Responsibilities include data mapping, transformation, cleansing, validation, reconciliation, testing, monitoring, and exception handling. Support Databricks Lakehouse architecture, security, common data models, reporting, analytics, and DevSecOps practices while coordinating mock migrations and UAT.

Develop and maintain full-stack web applications supporting U.S. Army mission requirements. Build backend services, APIs, business logic, databases, and modern web interfaces using .NET, SQL Server, JavaScript, HTML, and CSS. Modernize legacy applications, apply secure development practices, create database objects and documentation, participate in testing and code reviews, and collaborate with government and contractor teams. The role is primarily onsite at Rock Island Arsenal with limited work-from-home flexibility.

Embed with client teams to identify valuable AI opportunities and lead solutions from architecture through production deployment. Build integrations, permissions, hosting, data access, retrieval pipelines, agentic workflows, and evaluation systems. Set technical direction, defend architecture and security decisions, iterate solutions in production, mentor engineers, and help clients develop lasting AI capabilities.

The Data Engineer will develop and deploy data pipelines, create data models, and work with various data tools in Azure environments.

Leads the design and implementation of enterprise data platforms, pipelines, ingestion systems, and analytics products. Develops production-grade Python applications, manages AWS, Snowflake, and dbt environments, establishes engineering and governance standards, and improves observability, security, quality, and cost efficiency. Integrates disparate data sources and supports generative AI, RAG, and LLM applications while partnering with business, software, DevOps, and data science teams.

Designs, builds, and operates scalable batch and streaming data platforms using Spark, Python, SQL, Microsoft Fabric, and Lakehouse architectures. Develops ETL/ELT pipelines, dimensional models, data governance, observability, and CI/CD practices. Optimizes distributed workloads, supports AI and analytics use cases, leads technical design, and mentors engineers while partnering with architects, stakeholders, security, and governance teams.

Embed with client teams to identify AI opportunities and lead solutions from design through production deployment. Build secure integrations, data access, hosting, APIs, retrieval pipelines, and agentic systems; make architecture decisions; evaluate and iterate solutions; demonstrate production value; mentor engineers; and help clients develop their own AI capabilities.

Develop interactive dashboards, visual analytics platforms, and reporting solutions for SATCOM, network operations, and engineering data. Integrate AI/ML model outputs into user-facing analytics, establish visualization standards, guide architecture decisions, and collaborate with multidisciplinary engineering and data science teams. Mentor junior personnel and present technical insights to customers, senior leaders, and stakeholders.

Staff Software Engineer responsible for designing, building, operating, and improving distributed Java services and Python/PySpark data platforms. The role focuses on Kafka-based systems, Azure Databricks pipelines, Delta Lake, scalability, reliability, security, observability, and production operations. Responsibilities include technical ownership, incident response, monitoring and alerting, performance optimization, automated testing, data-quality controls, and incremental modernization. The engineer will participate in on-call rotations and independently resolve complex performance and reliability issues.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Lead enterprise-scale data architecture and engineering for Xometry’s Data Platform, including scalable batch and streaming pipelines, partner integrations, data models, governance, observability, and low-latency event-driven flows. Own the data plane connecting Xometry with Teamcenter and Designcenter, establish engineering standards, develop technical roadmaps, collaborate across teams, and mentor engineers. The role requires strong SQL, Python, cloud data warehouse, AWS, modern data tooling, CI/CD, testing, and enterprise integration expertise.

Own enterprise-scale data architecture and engineering for Xometry’s data platform, including real-time partner integration pipelines connecting manufacturing systems. Design scalable batch and streaming systems, data models, governance, observability, testing, and CI/CD practices. Lead multi-quarter technical roadmaps, collaborate across engineering, product, analytics, machine learning, and external partners, and mentor engineers. Support the DFM AI and IQE integration across Teamcenter, Designcenter, and related systems.

Build, deploy, and operationalize complex data pipelines and solutions using modern data platforms and ETL tools. Design data architectures, integrate sources, improve data quality, and support AI, machine learning, and business intelligence initiatives. Analyze and resolve complex data defects and incidents, apply governance and security practices, and serve as a data engineering subject matter expert. Guide team members, educate business users, communicate technical concepts, and provide technical leadership across enterprise data projects.

Build automated validation, reconciliation, anomaly detection, and test automation for cloud billing data across AWS, Azure, and Google Cloud. Investigate and resolve data-quality issues, automate preventive checks, document provider billing models, support customer-facing data questions, and use AI tooling to accelerate investigation and anomaly triage.

Build and lead Cognite’s enterprise data warehouse, semantic layer, data pipelines, integrations, and business data models. Use Azure, Microsoft Fabric, Power BI, Python, SQL, APIs, and Cognite Data Fusion to deliver reporting and AI-ready data solutions. Establish data governance, security, privacy, and quality standards while partnering with Finance, Sales, Marketing, HR, Legal, Engineering, and other stakeholders.

Build and operate production data pipelines across Oracle, Talend, and Apigee using SQL, PL/SQL, Java, and Python. Integrate acquisition and financial data, develop reusable data products and analytics datasets, maintain REST APIs, migrate legacy data, optimize workflows, and implement validation, governance, testing, documentation, and database design practices. Support Navy acquisition transformation in an IL-4/5 government environment.

Leads the design, governance, and integration of mission-focused data ontologies, knowledge models, taxonomies, and standards. Collaborates with analysts, engineers, architects, and data owners to establish interoperable data practices, support standards adoption, and integrate models into enterprise tools and workflows. Produces monthly posture reports and guides data architecture and approval processes for intelligence data enterprises.