Top Data Engineer Jobs in Portugal

Lead software engineering and reliability efforts for a data platform within an agile Corporate Technology team. Responsibilities include designing, developing, testing, troubleshooting, and reviewing secure production code; improving application resiliency and automating recurring remediation; promoting validated AI-assisted engineering practices; applying SDLC automation; and evaluating vendor and architectural solutions. The role requires strong software engineering, cloud-native, security, CI/CD, agile, operational stability, and financial services technology expertise.

Design and develop scalable backend software for data access across databases and data lakes. Build SQL generation and platform persistence capabilities, collaborate with product managers, own features through delivery, implement automated testing, participate in reviews, optimize performance, and improve reliability, observability, and production readiness.

Architect and build scalable distributed systems, data ingestion pipelines, and data lake platforms using Kafka, Apache Iceberg, and Apache Spark. Lead complex technical initiatives, establish engineering best practices, develop reusable libraries and frameworks, optimize JVM performance, troubleshoot production systems, and collaborate across teams to deliver reliable, high-performance platform capabilities.

Design, build, and maintain large-scale, fault-tolerant cloud data protection services for SaaS applications. Develop backup and recovery systems, APIs, administrative UIs, monitoring, automation, and build infrastructure. Troubleshoot scalability, stability, and critical production issues; collaborate with product and customer-facing teams; lead projects, conduct code reviews, and mentor junior engineers.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

Leads hands-on data engineering and software development for HR and employee experience data products. Designs scalable, modular data pipelines and cloud platforms using automation, testing, governance, security, and data quality practices. Drives modernization, operational excellence, cost management, and ease of data consumption while influencing architecture, product, and business decisions. Mentors engineers, supports SDLC accountability, and partners with cross-functional technology teams on complex, internet-scale data initiatives.

Designs, operates, and improves secure AWS cloud environments and ECS application platforms supporting data-driven applications. Builds and administers CI/CD pipelines using Jenkins and Spinnaker, improves reliability and scalability, supports Kafka and SQL dependencies, and partners with globally distributed Agile teams. The role also involves mentoring engineers, establishing reusable deployment standards, and driving operational excellence across Java/Spring Boot microservices and React micro frontends.

Leads the design, development, and ownership of scalable data systems, ELT pipelines, data models, and Snowflake architectures supporting analytics, experimentation, machine learning, and business decisions. Partners cross-functionally to deliver reliable, governed data solutions; improves quality, observability, testing, documentation, and operational practices; mentors engineers; and shapes technical direction through automation, technology evaluation, and AI-enabled engineering.

Build and own trusted data products, dimensional models, semantic layers, and dbt transformations that support analytics, experimentation, machine learning, reporting, and decision-making. Partner with cross-functional teams to translate business needs into scalable datasets, improve data quality and governance, establish engineering standards, and drive self-service analytics. Mentor engineers and analysts while leveraging AI tools to improve development, documentation, and quality.

Design, build, and operate WHOOP’s data platform infrastructure, including lakehouse, warehouse, and streaming systems. Improve performance, reliability, cost efficiency, observability, and developer experience across AWS-based data systems. Develop internal tools, APIs, automation, CI/CD, and infrastructure-as-code practices. Partner with data, ML, analytics, and software engineering teams to support scalable workflows, governance, and production excellence while using AI-assisted development tools effectively.

Build and operate AWS GovCloud data infrastructure across development, preproduction, and production environments. Establish PostgreSQL platforms, infrastructure-as-code with Chef, BI infrastructure, identity integrations, monitoring, observability, CI/CD, and disaster recovery. Support data pipelines, troubleshoot incidents, optimize databases, and ensure FedRAMP and FISMA compliance. Collaborate with security, compliance, DevOps, and data engineering teams while delivering greenfield infrastructure from architecture through production.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

As a Data Engineer at OneImaging, you'll design and maintain scalable data pipelines and infrastructure, collaborate on analytics, and ensure compliance with healthcare regulations.

Develop and maintain data cleaning, preprocessing, integration, and quality checks. Build dashboards and complex visualizations (Tableau or Power BI). Apply AI/ML techniques for unstructured data analysis, provide data-driven analysis, and perform quality assurance to support government/intelligence decision-making.

As a Senior Associate Data Engineer, you will build and maintain data pipelines, support analytics initiatives, and uphold data standards while collaborating with teams on data strategy and governance.

Lead and deliver secure, scalable data and AI/ML systems (including LLM-based and agentic architectures) for enterprise KYC and risk platforms. Define architecture and engineering standards, mentor engineers, drive AI-assisted development adoption, ensure observability, governance, and operational stability across cloud-native data platforms.

Lead and mentor data engineering teams to design, build, and operate a secure, scalable KYC and risk-assessment data platform. Develop production code for data-intensive systems, set architecture and engineering standards, govern LLM-based applications and agentic AI, drive adoption of AI-assisted development practices, ensure observability, security, and cloud-native operations, and advise cross-functional stakeholders.

Lead configuration and product data management across the airplane lifecycle, define and roll out CM/DM standards, conduct technical design reviews and audits, evaluate complex trades and changes, integrate cross-discipline product elements, develop technical program plans, and mentor engineering teams to ensure traceability, compliance, and adopt CM/DM tools and processes.

Leads the design and management of data engineering solutions, including pipelines, storage systems, data architecture, quality frameworks, APIs, and data extracts. Deploys solutions across Kubernetes, Teradata, AWS, and Databricks; selects storage technologies; maps data lineage; establishes QA standards; and collaborates cross-functionally to improve data quality, accessibility, and operational efficiency. Requires independent judgment and flexibility for nights, weekends, and variable schedules.

Lead the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Lead engineers, establish data architecture and pipeline best practices, develop and review high-performance code, and collaborate across Agile, product, security, and engineering teams. Mentor developers, support machine learning and microservices initiatives, and apply emerging technologies to deliver reliable data solutions.

Build and standardize ML Ops services, data pipelines, automation, monitoring, and governance for predictive and generative AI systems. Develop scalable pipelines and integrations across Databricks, AWS SageMaker, and AWS Bedrock; establish feature stores; optimize data processing; and support model deployment, inference, evaluation, and lifecycle management. The role also architects complex data systems, oversees CI/CD pipelines, enhances system performance, translates requirements into solutions, and mentors junior engineers.

Leads design, development, and operational ownership of a hybrid vendor product and enterprise AI/ML platform. Responsibilities include CI/CD, Infrastructure as Code, environment automation, release management, monitoring, observability, backup and recovery, capacity planning, production support, security, and reliability. The role provides technical leadership, establishes engineering standards, mentors colleagues, improves processes, and supports reusable, governed enterprise feature capabilities.

Leads data warehouse quality engineering by developing test strategies, plans, cases, and summary reports. Reviews data quality, ETL loads, transformations, extractions, and migrations across enterprise data warehouse systems. Manages testing risks, defects, environments, and resources while collaborating with technology, governance, risk, and business stakeholders. Provides guidance to junior engineers and supports continuous improvement. The role requires expertise in Hadoop, SQL/HQL, PySpark, AWS, data warehouse testing, automation, and quality assurance processes.

Develop and optimize validation KPI dashboards, Jira workflows, and Google Sheets tools. Use Python, Power BI, Databricks, and automation to analyze validation data, identify risks, and improve reporting. Create validation processes, documentation, training materials, and standard work while supporting Lean Six Sigma continuous improvement and change management. Maintain data repositories and help validation teams track readiness, accountability, and completion milestones.

Develop and deploy Python data pipelines and business data objects. Design software solutions, implement source-to-target mappings, build CI/CD pipelines, resolve complex technical issues, and perform unit and integration testing. The role also promotes software quality, engineering best practices, team improvement, and development of digital data capabilities.