Top Data Engineer Jobs in Portugal

Leads and mentors the Data Engineering team while remaining hands-on in designing, building, and maintaining scalable data pipelines, warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical delivery; and resolves complex issues. Partners with Finance and business stakeholders to translate data needs into practical solutions, sets technical direction, evaluates technologies, and communicates strategy, risks, and recommendations to leadership.

Designs, develops, and maintains enterprise-scale Apache NiFi data flows for secure data onboarding, integration, transformation, and governance. Develops custom Java processors, controller services, APIs, and validation components; integrates databases, APIs, messaging systems, Kafka, and cloud storage. Optimizes clustered pipelines, implements metadata and security controls, supports Zero Trust requirements, troubleshoots complex data movement issues, and mentors engineers while contributing to architecture, standards, and documentation.

Designs, develops, and maintains enterprise Apache NiFi data flows and custom Java processors for secure, scalable data integration. Responsibilities include building ingestion pipelines, integrating APIs, databases, message queues, Kafka, and cloud storage, managing metadata and governance, optimizing clustered deployments, and implementing Zero Trust security controls. The role supports complex data onboarding, transformation, validation, workflow automation, and enterprise architecture initiatives.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Leads and delivers complex data engineering projects, designing scalable data infrastructure, pipelines, warehouses, and data quality solutions. Recommends tools and architectures, optimizes data movement and performance, collaborates across technical teams, mentors engineers, and translates technical concepts for clients and nontechnical stakeholders. The role may also involve real-time processing, data modeling, transformation, security, and MLOps.

Design, develop, test, and maintain ETL solutions and data warehouse structures using SSIS and SQL Server. Build and support SSAS models, optimize ETL performance, ensure data quality, partner with stakeholders, and produce technical documentation to support BI and reporting initiatives.

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.

Supports cybersecurity engineering for avionics and government systems by assessing risks, developing requirements and controls, designing security architectures, applying NIST controls and STIGs, and supporting certification efforts. The role requires systems thinking, Linux and scripting experience, Cross Domain Solution knowledge, and eligibility for a security clearance. Desired qualifications include C, C++, Java, and Python experience, RMF familiarity, and willingness to travel up to 10%.

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 manage scalable ETL/ELT pipelines and data warehouses, ensuring performance, reliability, integrity, and consistency. Monitor systems, troubleshoot issues, collaborate with technical and business teams, document architectures, and improve data infrastructure for scalability. The role requires production-quality Python, AWS experience, strong debugging skills, and approximately four years of fintech or startup experience. It is based in Palo Alto with three days per week in the office.

Designs, builds, and maintains scalable Databricks and PySpark data pipelines; develops Python and SQL transformations; identifies insights from ambiguous datasets; and ensures data quality, governance, documentation, and reliable delivery. Collaborates with stakeholders and cross-functional teams to integrate data into applications and dashboards, troubleshoot pipeline issues, and support a Department of Defense Navy client.

Develop and operationalize data applications using Palantir Foundry for national security clients. Translate stakeholder needs into technical solutions, ingest and transform data, build workflows and applications, support scalable data platforms, and lead analytics projects. The role also involves advising clients on data-driven mission impacts, collaborating with Agile teams, and applying DevSecOps, AI/ML integration, security, and compliance practices.

Build and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include designing data solutions, analyzing and cleansing data, implementing governance and security practices, testing transformations, resolving defects, and collaborating across teams. The role requires Python, SQL, AWS, Databricks, Terraform, API design, and AI platform experience.

Builds and maintains scalable batch and streaming data pipelines supporting analytics, machine learning, and business operations. Responsibilities include writing Python, SQL, and Spark code; ensuring data quality and reliability; monitoring and troubleshooting pipelines; implementing testing and observability practices; documenting data architecture; and collaborating with analysts, data scientists, product managers, and engineering teams in an agile environment.

Build and maintain data pipelines in Azure Synapse using Spark, Python, SQL, and C#. Apply data warehouse and lakehouse modeling standards, collaborate with analysts and testing teams, troubleshoot data issues, and develop machine learning and regression analysis capabilities using Azure ML and related tools.

Build and operate Pivotal’s flight data warehouse and telemetry pipelines using AWS Glue, Athena, SQL, and Python. Design curated data marts, fleet health alerting services, data quality gates, dashboards, and query interfaces. Own data infrastructure, deployment, monitoring, and schema evolution end to end while collaborating with firmware, GNC, battery, flight operations, systems engineering, and MRO teams on telemetry and airworthiness decisions.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Develop and maintain Python and SQL processes for acquiring, transforming, normalizing, and loading large spatial datasets into databases. Evaluate commercial and open-source data quality, interpret product specifications, update ETL workflows, and support continuous geocoding data quality improvements. The role collaborates with product teams and manages multiple priorities in a fast-paced environment.

Design, build, and operate complex Apache Airflow workflows, Snowflake and dbt data transformation pipelines, and distributed data science inference systems across multi-cloud environments including AWS and GCP. Collaborate with an experienced data engineering team while applying systems engineering and product lifecycle principles.

Build and maintain scalable data platforms, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop scripts and programs to convert data into usable formats, support platform operations, and collaborate with analysts, engineers, developers, and data consumers in agile environments. Projects support missions including fraud detection, cancer research, and national intelligence.

Build and own Stuut’s data infrastructure, including ingestion pipelines, canonical data models, semantic layers, event pipelines, quality testing, observability, and DataOps practices. Partner with product, engineering, and ML teams to deliver reliable datasets, dashboards, KPIs, and insights. This foundational role will establish the company’s data architecture and scale it as customer adoption grows.

Engineer and maintain mission-relevant data ingest and distribution pipelines across multiple networks. Configure ETL workflows, COTS/GOTS applications, databases, cloud systems, and CI/CD infrastructure. Troubleshoot production systems, hardware, protocols, and network connectivity while ensuring data reliability, low latency, and integrity. Collaborate with software developers, systems administrators, external providers, users, and stakeholders. Maintain system documentation, diagrams, monitoring, and operational workflows in an agile environment.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms. Leads data engineers in integrating structured and unstructured data across secure environments, establishes engineering standards, ensures data quality, and supports analytics, reporting, and mission-critical decisions. Uses ETL, distributed systems, streaming architectures, cloud services, and access controls to deliver reliable enterprise data infrastructure.

Builds and maintains reliable, scalable, secure data platforms and cloud infrastructure. Responsibilities include troubleshooting Tier 3 production issues, designing data ingestion and transformation workflows, implementing CI/CD, monitoring systems, and supporting enterprise data operations. The role may involve Kubernetes, Kafka, NiFi customization with Java, AWS services, and classified Intelligence Community environments.

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.