Top Data Engineer Jobs in Portugal

Designs, implements, troubleshoots, and optimizes complex data flows using NiagaraFiles and Apache NiFi. Evaluates data formatting and software issues, processes Data Flow Management Requests, ensures compliance, and supports modernization efforts migrating flows to virtual cloud environments. Collaborates with engineering and operations teams to rebuild data flows, provides customer support, and participates in rotating on-call coverage for emergency system issues.

Supports data flow and routing systems engineering across government and Intelligence Community organizations. Plans and orchestrates data management, security coordination, metadata enrichment, and automated routing. Develops system requirements, evaluates data movement options, performs feasibility studies, documents system interfaces, and contributes to architecture, design, development, network implementation, testing, and modernization efforts. Manages enterprise data movement requirements through Jira.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Design, build, and operate large-scale, fault-tolerant real-time and batch data pipelines powering an ad-tech DSP. Collaborate with engineers and data scientists to define data models and SLAs, optimize performance and capacity, enforce data quality and monitoring, mentor engineers, and evaluate new data technologies to scale analytics and ML workloads.

Develop and lead enterprise Oracle Fusion HCM extensions, integrations, reports, workflows, and user interfaces using VBS, APEX, JavaScript, REST APIs, and BI Publisher. Guide development teams, enforce configuration-first and upgrade-safe practices, conduct architecture reviews, support Agile delivery, troubleshoot production issues, and mentor junior developers. The role supports federal HCM modernization, agency migrations, deployments, and technical go-live activities.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Designs and optimizes batch and streaming data pipelines, cloud data warehouses, dimensional data models, and ETL/ELT processes at terabyte scale. Troubleshoots pipeline issues, improves infrastructure reliability and performance, implements data quality and governance practices, and supports privacy compliance. Collaborates with analysts, data scientists, and stakeholders to translate business needs into technical solutions. Leads and mentors data engineers, documents systems, and evaluates emerging technologies.

Own and evolve Stream’s end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment.

Own Mercor’s data platform infrastructure across Snowflake, Fivetran, Airflow, Astronomer, and Hex. Responsibilities include data organization, role-based access control, row-level security, PII masking, governed migrations, pipeline reliability, on-call support, dashboard governance, warehouse cost optimization, and CDC or streaming ingestion. The role also establishes platform standards and builds internal tooling for a new Data Platform team.

Build and own the unified data layer: ingestion pipelines into ClickHouse, data modeling and entity resolution, canonical metric definitions, quality checks, and internal data tools. Support ad-hoc analyses and work closely with engineering to ensure stable, trustworthy data powering customer dashboards and product BI.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Design and operate a scalable data platform for high-volume robotics telemetry and control-plane data. Responsibilities include data capture, storage tiers, ingestion, transformation engines, durable processing, schema and ID-key management, retention and egress policies, replication, CI/CD, monitoring, and alerting. The role supports cloud, on-premises, edge, and outage-resilient deployments while enabling downstream analytics and machine learning.

Build scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog. Ingest batch and streaming data across medallion architecture layers, develop transformations and data quality rules, and implement orchestration, monitoring, alerting, and automation. The role requires cloud data engineering, data modeling, performance tuning, CI/CD, and strong knowledge of lakehouse architecture, governance, and distributed systems.

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.

Supports production systems, applications, data workflows, and automation. Develops Python services and scripts, maintains ETL pipelines, supports Java/J2EE applications, and administers Linux/Unix environments. Builds Terraform and AWX automation, improves reliability and performance, manages monitoring and deployments, troubleshoots operational issues, and collaborates with engineering and business teams. The role also contributes to planning, technical requirements, continuous improvement, mentoring, and AI-assisted engineering practices.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As a Senior Data Engineer, you will design and develop AI-powered data solutions, investigate tools, and collaborate with engineering to enhance Sleuth's platform.

The Lead Data Engineer will design, implement, and maintain data pipelines for mission-relevant data, ensuring compliance with security frameworks and optimizing data accessibility and interoperability across mission systems.

Design, build, and deploy data pipelines, databases, and AI-enabled automation to improve manufacturing test throughput, yield, and reliability. Integrate test systems and IoT sensors, support root-cause analysis, develop dashboards and alerts, and work hands-on with production teams for on-floor troubleshooting and sensor/validation tasks.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

Embed with the Asia Commodities desk to own vendor data feeds, build scalable data pipelines and dashboards, transform market and supply/demand data into analytical constructs, support research-to-production workflows, and collaborate with investors and platform teams to enable systematic commodities strategies and trading decisions.