Top Data Engineer Jobs in Portugal

Manage Oracle and PostgreSQL databases across on-premises and AWS environments, including upgrades, migrations, performance tuning, security, and backup recovery. Design and maintain ETL pipelines using Talend, SQL, Power Automate, and related tools. Perform data profiling, quality analysis, governance, and cloud migration activities. Create technical architecture and lineage documentation while supporting regulated, audit-ready data platforms and integrations.

Develop and optimize multilingual machine translation and speech models for low-resource languages. Build and manage multilingual datasets, scalable data pipelines, and end-to-end MLOps workflows. Deploy cloud-native machine learning solutions on AWS using containerized environments and CI/CD practices. Conduct model testing, benchmarking, monitoring, troubleshooting, and lifecycle management while researching emerging NLP, speech, generative AI, and machine learning technologies.

Designs and operates scalable data pipelines and platforms for analytics, machine learning, and Agentic AI. Develops production-grade Python and SQL solutions, implements data quality and monitoring, supports end-to-end ETL/ELT workflows, and partners with data scientists, analysts, and business stakeholders. The role also includes code reviews, testing, source control, production support, mentoring, and establishing engineering best practices. Familiarity with Celonis, SAP, and Salesforce is preferred.

Owns the architecture and development of pricing data pipelines, models, platforms, and automation. The role supports price waterfall analysis, discount optimization, segmentation, margin analytics, data quality, near-real-time insights, and machine learning use cases. Responsibilities include production Python development, SQL-based ETL/ELT, cloud pipeline optimization, testing, monitoring, source control, technical mentoring, and collaboration with pricing, commercial, and technical stakeholders.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Supports the Fraud Data Engineering team through data integration, quality validation, analytics, documentation, and platform modernization. Responsibilities include gathering requirements, creating business requirement documents and source-to-target mappings, enabling batch and real-time integrations, analyzing large datasets, documenting data flows, participating in Agile activities, and developing Databricks notebooks and validation frameworks. The role is a 12-week internship focused on fraud and security data solutions.

Supports enterprise data engineering initiatives involving real-time event streaming, Voice of the Member analytics, master data management, data quality, metadata, and AI technology proofs of concept. The intern analyzes data flows and feedback, assists with platform governance and automation, researches technology trends, collaborates with engineers and stakeholders, and presents findings. The 12-week program requires current enrollment in a technical degree program with graduation in December 2027 or later.

Designs and supports testing of industrial screw pumps for defense applications. Collects, databases, visualizes, and analyzes CMM and pump test data; develops regression and predictive models; automates reporting; documents nonconformances; and creates dashboards for stakeholders. Collaborates with design, manufacturing, quality, sales, and program teams to improve product robustness, testing, engineering standards, and manufacturing processes. Provides customer support and contributes to technical documentation and process improvements.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Designs, deploys, and manages cloud infrastructure using OpenStack and comparable platforms. Supports Linux and Python-based software engineering, IT management, system engineering, network automation, cloud security, and operations. The role requires substantial software engineering experience, strong communication skills, and an active TS/SCI clearance with polygraph.

Manage data requirement and electrical change requests for the U.S. Navy Strategic Weapon System. Coordinate with contractors, lead pre-coordination meetings, develop and drive change requests, perform impact reviews on SPALT and contractor documentation, support technical resolution with SSP working groups, and implement changes into SSP documentation. Limited off-site travel may be required.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Build and govern Anthropic’s quote-to-cash data foundation. Design canonical models from Salesforce, CPQ, and billing data; establish data integrity standards and SLAs; collaborate with systems engineers on schema changes and ingestion; and create data products, dashboards, and self-service analytics tools for GTM and Finance teams. The role also owns data architecture, translates stakeholder needs into technical requirements, and influences roadmaps.

Design and maintain automated testing across data pipelines, ETL/ELT workflows, REST APIs, backend services, and cloud data warehouses. Validate data integrity, schemas, transformations, reconciliations, performance, and reliability for ERP data flows. Integrate tests into CI/CD, manage synthetic test data, investigate discrepancies, support releases, and mentor junior quality engineers while promoting data-quality-focused testing practices.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Leads customer-facing enterprise data engineering engagements for federal clients, designing scalable data architectures, pipelines, integrations, APIs, object storage solutions, and Java-based applications. Responsibilities include architecture workshops, technical implementation, troubleshooting, documentation, customer communication, mentoring junior engineers, and service innovation. The role requires active Top Secret clearance, at least eight years of relevant experience, strong consulting skills, and expertise across enterprise software, data engineering, and integration technologies.

Provides data engineering and analysis consulting for a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least three years of relevant experience. Preferred qualifications include Palantir, visualization, big data, programming, ETL, consulting, and Agile experience.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

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.