Top Data Engineer Jobs in Portugal

Designs, builds, optimizes, and maintains data pipelines, ETL/ELT workflows, data platforms, and data models. Supports high-volume data processing, cloud and hybrid modernization, data quality, metadata, lineage, governance, and analytics-ready datasets for federal mission and AI/ML applications. Collaborates with data scientists, architects, and analysts while producing technical documentation. The role may be filled across junior, mid-level, senior, or principal career levels.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Owns backend software engineering for a data platform supporting federal disability claims. Designs APIs, internal tools, shared Python libraries, and services; establishes architecture, testing, packaging, documentation, and code-quality standards. Partners with data engineers, leads reviews, resolves complex defects, mentors engineers, and supports planning. Requires substantial Python, pytest, API, software design, and mentoring experience, along with U.S. citizenship and Public Trust eligibility.

Build and maintain reliable data pipelines, APIs, integrations, and event-driven or batch processing systems. Transform data from advertising, ecommerce, order management, and event sources while improving monitoring, freshness, performance, and scalability. Collaborate with data engineering, infrastructure, product, and business teams to extend the integration platform in a cloud-native, multi-tenant environment.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Design scalable API-based ETL, event-driven and batch processing systems; establish standards for observability, freshness, and failure handling; and guide build-versus-buy decisions. Collaborate across engineering, infrastructure, product, and business teams while mentoring engineers, leading design reviews, and driving complex platform initiatives to completion.

Build and operate AI-first full-stack applications, internal tools, service connectors, automations, and data products supporting business functions. Translate operational and financial reconciliation needs into scalable software solutions, integrate business systems, improve engineering processes, and own products across the full lifecycle. The role requires production engineering experience, data warehouse and SQL expertise, and familiarity with frontier AI tooling, with opportunities to evaluate LLM systems and work with platforms such as Snowflake, dbt, and enterprise business applications.

Build and ship a visualization product for datasets, collection, quality, evaluations, and live pipeline health. Responsibilities include developing web or CLI interfaces, enabling fast exploration of video, pose, batches, coverage, failures, and statistical summaries, and partnering with research and operations teams. The role emphasizes performance, shareability, high-volume media, and production-quality internal tools in a zero-to-one environment.

Lead the end-to-end data platform for large-scale physical-labor video, from on-device capture and reliable uploads over poor networks through object storage, compression, training-shard creation, dataset versioning, and delivery to research customers. Own petabyte-scale media infrastructure, cost and throughput optimization, observability, and ingestion contracts across firmware and international operations. The role requires strong software engineering and production experience with media or sensor data at significant scale.

Builds data pipelines, models, analytics solutions, AI and machine learning models, and Power BI dashboards using structured and unstructured learning data. Partners with faculty to gather requirements, generate insights into student behavior, apply statistical and diagnostic analysis, and provide dashboard training and analytics knowledge transfer to staff.

Develop technical enablement content, product demonstrations, solution guides, workshops, and training for HPE Apstra data center networking. Support sales, systems engineering, partners, and customers through presentations, technical expertise, and field enablement. Collaborate with engineering, product management, marketing, and regional teams on launches and positioning. Translate complex networking, automation, AI, and cloud infrastructure concepts into compelling technical narratives and customer value.

Designs and builds scalable data platforms and pipelines for scientific, clinical, manufacturing, and process-engineering data. Responsibilities include developing batch and streaming ETL/ELT systems, integrating Databricks, Snowflake, cloud platforms, MES, SCADA, and laboratory systems, and enforcing governance and GxP compliance. The role provides hands-on technical leadership, establishes data engineering standards, and collaborates with scientists, process engineers, and platform teams. It requires three days onsite weekly in Indianapolis and unrestricted U.S. work authorization.

Own the architecture, reliability, governance, and evolution of the Databricks lakehouse. Design batch and streaming pipelines, develop SQL/Python/PySpark transformations, productionize analytical and AI prototypes, and implement testing, orchestration, monitoring, data-quality controls, governance, and access management. Collaborate with analysts, Engineering, and Product to deliver scalable, cost-effective data solutions for clinical trial insights.

Designs, integrates, tests, troubleshoots, and evaluates optical transport networks for intelligence community customers. Maintains network configuration data, analyzes performance and risks, coordinates providers and vendors, develops circuit specifications and test methods, supports service agreements, estimates bandwidth requirements, and provides technical guidance for network operations and special projects.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, test, document, and maintain scalable big-data solutions. Evaluate and deploy tools and frameworks, develop APIs and integrations, translate requirements into designs, collaborate across teams, participate in code reviews, and provide production support and documentation.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

The Data Governance Engineer advances data governance initiatives, focuses on metadata cataloging, lineage harvesting, and governance workflows, and collaborates on data quality and compliance.

Lead the technical strategy for Coinbase's Data Platform, architecting data systems for AI/ML workloads and ensuring data reliability, performance, and cross-functional alignment.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.

Designs, develops, and optimizes enterprise Snowflake data platforms, pipelines, data models, and ELT/ETL workflows. Builds reliable data lake and warehouse solutions, manages Snowflake performance and costs, implements data quality and governance practices, and supports cloud data environments on Azure or AWS. Partners with analysts, scientists, engineers, and business stakeholders to deliver scalable solutions, troubleshoot platform issues, influence architecture, and mentor junior engineers.

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Develop and maintain embedded vehicle control and data planes for cloud-connected ECU fleets. Responsibilities include configuration delivery, vehicle data collection, distributed coordination, cloud integration, observability, reliability optimization, remote diagnostics, production support, open-source contributions, technical leadership, and mentoring. The role requires expertise in C/C++, Linux, Android, QNX, distributed systems, vehicle protocols, AUTOSAR, and fleet management.