Top Data Engineer Jobs in Portugal

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

Build and maintain reliable ETL and real-time data pipelines, enable data-driven product decisions, ensure relevant event collection, align data roadmap to business goals, and collaborate with platform, security, and analytics teams to deliver actionable insights.

Designs and delivers scalable data center network connectivity, including high-density fiber and low-voltage cabling plants supporting Ethernet, InfiniBand, RoCE, HPC, and machine learning systems. Responsibilities include procurement, construction oversight, network upgrades, connectivity testing, ISP circuit deployment, vendor troubleshooting, documentation, and standards compliance. Collaborates with network, construction, capacity planning, reliability, and operations teams. Requires after-hours support, travel, physical work, and ITAR eligibility.

Supports systems engineering lifecycle activities including requirements development, system design, implementation, integration, testing, documentation, workflow creation, and integration planning. Collaborates with experienced engineers on mission-critical remote sensing systems while developing technical communication, analytical, and systems engineering skills.

Develops and maintains healthcare data integration and EDI solutions, including ETL processes, data pipelines, data validation, cleansing, transformation, reporting, and compliance testing. Troubleshoots applications, manages code reviews, documents systems, and collaborates with analysts, healthcare providers, business partners, and IT teams. The role also implements security measures and recommends improvements to integration processes.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

Build and optimize scalable data pipelines that ingest autonomous-vehicle sensor data, localize it against HD maps, and produce high-quality labels. Develop workflow orchestration, data models, testing strategies, and production tooling. Investigate pipeline issues, support reliable releases, and own features from design through documentation. Collaborate with ML, robotics, data engineering, annotation, product, and mapping teams.

Support aircraft sustainment and logistics initiatives through data collection, ETL pipeline development, exploratory and statistical analysis, predictive modeling, knowledge graph capabilities, and production model integration. Monitor data quality and model performance while collaborating with engineering teams and government stakeholders in an Agile environment. The role requires NLP, Databricks, PySpark, SQL, predictive modeling, data governance, visualization, and an active Secret clearance.

A 12-week paid internship supporting data science engineering initiatives. The intern will learn business requirements, map data architecture, research algorithms, collaborate cross-functionally, and build, test, and refine data pipelines, predictive models, or software tools. The role provides mentorship, networking, professional development, and hands-on experience developing AI-enabled systems that create business impact.

Build and integrate a unified analytics platform across heterogeneous applications and data environments. Responsibilities include designing APIs, event streams, data contracts, shared platform services, ETL/ELT pipelines, presentation layers, cloud infrastructure, and infrastructure-as-code. The role partners with distributed data teams, evaluates consolidation and federation strategies, supports deployment reliability, and communicates technical tradeoffs through architecture documents and business cases.

Design, maintain, and troubleshoot Databricks notebooks, CI integrations, and GitLab repositories. Build APIs and integrations (Databricks, GitLab, Confluence), resolve access and Python library issues, support auditing and engineering standards, and assist leadership with evaluating and implementing tools to improve engineering visibility and workflows.

Designs, builds, and operates reliable Azure data pipelines using Databricks, Azure Data Factory, PySpark, Spark SQL, and Delta Lake. Responsibilities include batch and streaming ingestion, ELT transformation, dimensional modeling, data quality enforcement, CDC, schema management, monitoring, alerting, governance, CI/CD, documentation, and operational support. Partners with architects, BI developers, and stakeholders to deliver analytics-ready datasets and improve pipeline performance, reliability, and cost efficiency.

Designs, develops, and analyzes data management systems for a U.S. government customer. Builds batch and streaming data pipelines, performs ETL on large unstructured datasets, and maintains CI/CD pipelines for data and platform services. Collaborates with developers, data scientists, subject matter experts, and cyber analysts. Uses Kafka, Spark, Prefect, Docker, Kubernetes, Linux, and DevOps practices.

Designs and supports network services, automates tasks, monitors performance, troubleshoots incidents, and collaborates with teams on access controls. Must be available for urgent incidents.

Supports and enhances enterprise data systems, databases, integrations, and analytics platforms. Responsibilities include SQL development, database administration, performance tuning, production troubleshooting, backup and recovery, data pipeline development, Microsoft Fabric support, Power BI reporting, data modeling, security, governance, and documentation. The role collaborates with application developers, infrastructure teams, analysts, business stakeholders, and vendors to deliver reliable, scalable enterprise data solutions.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Build and operate Danaher’s enterprise data platform automation and reliability layer. Responsibilities include Infrastructure-as-Code, CI/CD, self-service developer enablement, data platform provisioning, observability, SLOs, auto-remediation, FinOps, compliance guardrails, and event-driven operations across Snowflake, Azure, Matillion, dbt, and Airflow. The role also develops AI-driven operators and copilots using Azure AI Foundry, Anthropic Claude, and related agent frameworks.

Operate and maintain the Performance Team's data platform: ensure reliability, correctness, and performance of ETLs and pipelines; implement quality checks, alerting, runbooks, and automation; troubleshoot GitLab CI/CD and resource bottlenecks; continuously tune workloads for efficiency.

Analyze product, engineering, and machine data; build Python and SQL analyses; manage ETL pipelines across Amazon S3, Google Cloud Storage, and BigQuery; and develop Tableau or Streamlit dashboards. Translate stakeholder needs into KPIs, metrics, reliable datasets, and actionable insights while monitoring data quality, troubleshooting pipeline issues, and enabling self-service analytics.

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Lead the architecture, circuit design, integration, verification, tapeout, and silicon validation of high-speed ADCs, DACs, and analog front-ends for advanced mixed-signal SoCs. Drive architecture trade studies, calibration techniques, FinFET CMOS implementation, converter integration, simulation methodologies, and collaboration with RF, digital, systems, package, and verification teams. Provide technical leadership and mentorship to analog and mixed-signal engineers.

Leads the architecture, circuit design, integration, verification, tapeout, and silicon validation of high-speed ADCs, DACs, and analog front-ends for advanced mixed-signal SoCs. The role covers converter trade studies, FinFET CMOS circuit implementation, calibration, clocking, references, signal integrity, and DSP collaboration. It also provides technical leadership, establishes design methodologies, and mentors analog and mixed-signal engineers.