Top Data Engineer Jobs in Portugal

Designs and leads cloud-native data engineering and architecture solutions for Supply Chain analytics. Responsibilities include building AWS data pipelines and lakehouse platforms, implementing Apache Iceberg and Medallion Architecture, developing Redshift data products, applying data mesh and governance principles, integrating SAP and Blue Yonder data, and leading technical engineering teams.

Build and operate scalable backend infrastructure for financial data, including ingestion, transformation, integration, and serving systems. Design high-performance distributed pipelines handling millions of documents and datapoints, support AI and retrieval systems, and integrate heterogeneous financial sources. Ensure secure, compliant, reliable handling of customer data while collaborating with globally distributed engineering teams and influencing technical direction.

Build and integrate deep learning models into software pipelines, collaborate with product and engineering teams, support data ETLs, and deploy solutions to cloud-hosted environments.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Build and operate Figma’s shared data platform supporting analytics, data science, and AI products. Responsibilities include improving development, testing, CI/CD, deployment, monitoring, data quality, incident response, performance, governance, lineage, and self-service data pipeline workflows. The role partners cross-functionally to establish reliable patterns for batch, micro-batch, reverse ETL, and product-facing data delivery.

Design, build, and operate scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develop PySpark and Structured Streaming workloads, optimize performance, establish data quality and governance standards, and support reliable enterprise data products. Collaborate with security, infrastructure, observability, and platform teams on data source onboarding, CI/CD, monitoring, access controls, and platform optimization.

Lead end-to-end development and deployment of enterprise generative AI and LLM applications, including agentic workflows, MCP-based data integrations, secure tool usage, AI gateways, identity propagation, access controls, and observability. The role partners with Information Security and corporate functions to deliver governed AI automation across procurement, supply chain, legal, finance, HR, and manufacturing.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.

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.

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.

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.