Top Data Engineer Jobs in Portugal

Lead data engineering initiatives involving large-scale data solutions, cloud-native applications, ETL pipelines, data architectures, CI/CD, and machine learning deployment. The role includes mentoring engineers, ensuring code quality, documenting operational processes, supporting consulting teams, engaging clients, contributing to proposals, and delivering analytics solutions globally. International client travel may be required.

Supports the development, maintenance, and monitoring of scalable ETL/ELT data pipelines in secure environments. Responsibilities include ingesting, cleaning, transforming, and validating structured and unstructured data; working with relational and NoSQL databases; building Docker modules; performing security scans and unit tests; and participating in Agile ceremonies and code reviews.

Architects and leads enterprise-scale data platforms, data mesh topologies, lakehouse infrastructure, and real-time analytics for classified environments. Establishes data architecture strategy, security and encryption standards, zero-trust governance, multi-cloud and air-gapped deployment strategies, and enterprise data modeling practices. Serves as the principal technical authority and advises government and executive stakeholders on mission-critical data capabilities.

Leads architecture, scaling, governance, and implementation of enterprise data platforms across multi-cloud and air-gapped environments. Designs batch and streaming systems, optimizes Spark and Trino/Presto clusters, implements Terraform-based infrastructure, establishes data quality, lineage, security, and compliance controls, and leads Agile engineering pods while mentoring staff. Requires advanced expertise in distributed data systems, cloud platforms, Kubernetes, programming, and regulated defense environments.

Designs and deploys end-to-end data infrastructure, including schemas, warehouses, automated pipelines, data quality monitoring, and curated datasets. Builds Power BI solutions for self-service analytics and partners with product, marketing, sales, data science, and machine learning teams. Applies analytical techniques, productionizes machine learning and LLM use cases, and translates complex data into business insights.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure Department of Defense environments. Develops reproducible, tested, containerized data and ML services using Python, Spark, Databricks, Docker, Terraform, SageMaker, and MLflow. Performs exploratory analysis, visualization, data storytelling, model evaluation, and decision-support development while collaborating with product, software, platform, DevSecOps, data science, and mission teams.

Participate in an internship focused on database modeling and optimization, data analysis and consolidation, data quality, and data support. Work alongside experienced professionals on real-world projects while developing technical and professional skills. The program requires prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.

Build and evaluate machine learning models that generate high-definition maps from raw sensor data. Develop statistical evaluation methods, scalable distributed data-processing pipelines, and backend infrastructure for global mapping. Collaborate with onboard engineering teams to improve mapping formats, manage the full modeling lifecycle, and measure system-wide impact. The role emphasizes autonomous vehicle mapping, large-scale systems, quantitative experimentation, and end-to-end software engineering ownership.

Design and implement scalable AWS and Databricks cloud architectures, ETL/ELT pipelines, lakehouse solutions, and data platforms. Responsibilities include developing PySpark and Python workflows, optimizing Spark performance and cloud costs, managing infrastructure with Terraform, implementing CI/CD, integrating diverse data sources, and ensuring data quality, governance, security, monitoring, and compliance. The role requires onsite presence in St. Louis three days per week.

Design, build, and operate end-to-end ETL/ELT pipelines to ingest network telemetry into a Data Mart. Create high-performance dimensional models, implement automated data quality and observability, enforce governance and IAM, maintain metadata and lineage, and enable analysts with production-ready datasets and standardized metrics.

Design, develop, and maintain SSRS reports and Power BI dashboards; perform data modeling and DAX calculations; support ETL (SSIS) and validate data across EMR/billing/CRM systems; gather and translate stakeholder requirements; liaise with offshore teams; perform testing, documentation, and ensure HIPAA/PHI compliance and data governance.

Design, build, and maintain scalable Spark/Python data pipelines and automated workflows. Enforce data governance, quality, lineage, and observability. Support ML pipelines (feature engineering, validation, monitoring) and document data assets, contracts, and standards.

Lead technical strategy, architecture, and modernization of enterprise master data and data lakehouse platforms. Design and implement large-scale data pipelines, databases, and distributed systems using Python, Spark, Redshift, Iceberg and AWS services. Ensure performance, reliability, governance, CI/CD, and AI-enabled engineering practices while mentoring senior engineers and aligning cross-functional stakeholders.

Deploy and commission control systems for greenfield data centers, including BMS, EPMS, PLCs, SCADA, Ignition, Rockwell controls, networking, and instrumentation. Perform device point-to-point checks, configure communication protocols, calibrate equipment, validate functionality, and support commissioning. Provide on-site guidance to subcontractors and collaborate with large project teams. Candidates must be willing to relocate to a project site for at least one year.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Build and improve an AI intelligence platform that transforms structured and unstructured data into business intelligence. Develop scalable data pipelines, dashboards, reporting systems, analytical models, AI workflows, prompt strategies, evaluation frameworks, and automation. Analyze restaurant and social media data to identify trends and opportunities, while monitoring quality, troubleshooting issues, and improving reliability and scalability. Establish best practices for AI-assisted analysis and mentor team members.

Designs, builds, and sustains data architecture, pipelines, ETL processes, and decision-support products for USPACOM’s MRT-C baseline. Ensures compliance with DoD data standards, Azure requirements, classification controls, and cross-domain information sharing needs. Integrates and validates terrain data, conducts quality assurance, produces baseline reports, and supports resource proposal development. The role requires advanced computational, software development, data analysis, machine learning, and cloud technology expertise in a national security environment.

Designs AI/ML data-fusion and sense-making solutions for intelligence and space missions. Develops architectures, testing and validation frameworks, responsible AI practices, adoption roadmaps, and mission applications across diverse data modalities. Collaborates with business development and program teams, mentors technical staff, and communicates AI capabilities and limitations to leadership and customers. Requires defense or intelligence experience, Secret clearance with ability to obtain TS/SCI, and full-time onsite work.

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.