Top Data Engineer Jobs in Portugal

Design, build, and operate high-scale advertising data systems processing billions of events through real-time and offline pipelines. Develop reliable, maintainable features, review code, automate deployment, support live services through on-call rotations, and optimize distributed-system performance, latency, and capacity. Collaborate with advertising stakeholders to define requirements and ensure security, compliance, testing, and quality standards.

Designs and scales enterprise backend services, data platforms, pipelines, and distributed processing solutions. Leads architecture, graph-based data initiatives, Snowflake implementations, engineering standards, code reviews, CI/CD, testing, and AI-assisted development. Partners with technical and business stakeholders, ensures security and regulatory compliance, optimizes platform performance, and mentors engineers.

Design, build, deploy, and support production data pipelines and enterprise data solutions in Databricks and GCP. Develop data models and layered architectures, ensure data quality, security, and governance, integrate multiple sources, and support analytics, AI/ML, reporting, and operational decision-making while collaborating with technical teams and federal stakeholders.

Leads the architecture, development, deployment, and operation of scalable production data pipelines. Defines data strategy, integrates enterprise data solutions, ensures governance and performance, communicates with executives, and mentors engineers. The role provides technical leadership in a growing practice and requires expertise across cloud platforms, orchestration, ETL/ELT, lakehouse architectures, and regulated-industry data environments.

Build and scale resilient ingestion, catalog, and derived data pipelines powering core infrastructure datasets. Own data contracts, schema governance, quality gates, automated testing, observability, and pipeline reliability. Modernize legacy systems into scalable platform architecture, including a graph database layer. Partner with global engineering, data science, ML, intelligence, and trust teams to support matching, confidence scoring, and automated workflows.

Develops advanced data engineering and visual analytics applications for structured and semi-structured data. Designs enterprise database systems, builds large relational databases, develops and optimizes data pipelines, integrates systems with existing data warehouses, and improves system performance. Requires Java, AWS, ETL, Spark, Scala, SQL, PostgreSQL, NiFi, Informatica, and Linux experience, along with an active Top Secret/SCI-eligible clearance.

Architect and maintain secure, scalable data platform infrastructure and resilient batch and streaming pipelines. Build infrastructure using Terraform and Helm, deploy cloud data services, optimize schemas and database performance, and implement monitoring and alerting. Partner with application teams to resolve data issues, establish governance practices, and support MLOps through model versioning, containerization, and deployment.

Build and oversee reliable ETL/ELT pipelines and data products that transform complex customer data into trusted analyses, visualizations, quality reports, and AI-powered insights. Responsibilities include data exploration, quality control, anomaly detection, AI/ML output evaluation, customer collaboration, documentation, and improving data-cleaning and analytics tools. The role requires cross-functional work in an Agile environment focused on security, data integrity, and clear communication.

Build and maintain ETL/ELT pipelines, explore and clean complex customer data, and deliver trusted analytics products, reports, visualizations, and AI-powered data capabilities. Partner with customers and internal teams to define data requirements, investigate discrepancies, validate AI/ML outputs, implement quality controls, and improve data tooling through automation. Translate complex findings and workflows into clear narratives and repeatable processes.

Architect and maintain secure cloud data infrastructure, scalable schemas, and resilient batch and streaming pipelines. Deploy data platforms using Terraform, Helm, CI/CD, and infrastructure-as-code practices. Build monitoring and alerting for data health, system performance, and container observability. Troubleshoot application workflows, database queries, and pipeline logic while advancing data governance. Support MLOps through AI model versioning, containerization, and deployment.

Monitors and maintains critical data center systems, including mechanical, electrical, cooling, generator, fire suppression, and UPS equipment. Performs inspections, preventive maintenance, troubleshooting, installations, work order management, documentation, inventory tracking, and incident response. The role follows safety and regulatory procedures, coordinates with vendors and contractors, supports equipment upgrades, and works scheduled shifts, holidays, and rotating on-call coverage in an on-site environment.

Support data centre operations through installation, migration, maintenance, troubleshooting, break-fix, and decommissioning of compute, networking, storage, and high-density AI infrastructure. Perform smart hands support, cabling, site surveys, documentation, safety procedures, and on-call incident response while collaborating with engineering, project management, customers, and vendors to maintain uptime and service levels.

Develop machine learning and data science solutions for intelligence operations and special operations missions. Build geospatial and 3D computer models, select and tune machine learning algorithms, create predictive models, conduct quantitative analyses, and visualize data. Collaborate with clients and guide algorithm and system development using disparate data sources and modern data and computing tools.

Designs, develops, and maintains IBM Planning Analytics and TM1 models, cubes, applications, rules, feeders, and TurboIntegrator processes for financial planning, forecasting, reporting, and actuarial modeling. Builds data ingestion, transformation, quality, masking, and curation processes; supports SQL development, security, performance monitoring, and critical data pipelines. Partners with finance and actuarial stakeholders, provides operational support, develops scalable tested code, and mentors team members.

Designs, builds, and operates large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark applications; managing streaming workflows; working with relational and NoSQL stores; orchestrating pipelines; troubleshooting distributed systems; and supporting reliable, scalable production data platforms.

Software Engineer responsible initially for planning and executing SaaS data migrations, building extraction, transformation, mapping, validation, and automation tooling, and supporting technical escalations. The role evolves into full-stack feature development and platform operations across Angular, .NET microservices, cloud infrastructure, CI/CD, testing, observability, and event-driven systems. Requires strong .NET/C#, SQL Server, Entity Framework, migration, frontend, and distributed systems experience.

Designs, builds, tests, and deploys Appian business applications supporting workflow automation and data integration initiatives. The role requires collaboration, documentation, analytical ability, and experience with data products, Appian development, REST APIs, SQL, GitHub, and Agile delivery.

Design and implement scalable data architectures, ingestion pipelines, ETL processes, data models, governance standards, and legacy integrations. Improve data reliability, efficiency, and quality while advising clients on data management and processing. Collaborate with architects, data scientists, and IT teams to deliver high-performance solutions, algorithms, prototypes, and proofs of concept. The role is hybrid in Madrid or Barcelona and requires residence in Spain with legal work authorization.

Leads the design, administration, optimization, and modernization of enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Responsibilities include database operations, high availability and disaster recovery, performance tuning, replication, cloud migrations, monitoring, security, governance, and automation. The role collaborates with architects and infrastructure teams, mentors junior engineers, and serves as a subject matter expert for database infrastructure and cloud architecture.

Designs, builds, and maintains scalable ETL/ELT pipelines and finance data models using Snowflake, AWS, dbt Core, Python, and SQL. Develops Medallion architecture data marts, integrates ERP systems, and partners with stakeholders on financial workflows. Implements data governance, RBAC, automated quality testing, observability, documentation, CI/CD, and secure deployments. Monitors Snowflake costs and optimizes queries while supporting Oracle EBS integrations and the migration to Workday.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

Leads the architecture, development, modernization, and operation of enterprise data platforms and pipelines supporting credit risk, analytics, regulatory reporting, and AI initiatives. Builds scalable cloud lakehouse solutions using Databricks, Spark, Delta Lake, Python, and SQL; establishes governance, quality, lineage, observability, and compliance controls; optimizes performance and costs; leads technical initiatives; influences architecture; mentors engineers; and partners with product, analytics, science, engineering, and business stakeholders.

Leads the design and management of scalable cloud database infrastructure across AWS and Azure. Responsibilities include PostgreSQL and SQL Server administration, GraphQL and Hasura API development, cloud migrations, CI/CD automation, infrastructure as code, performance monitoring, security, compliance, data modeling, and mentoring engineers. The role also supports data access layers for AI and application workloads.

Owns architecture, roadmap, development, and production operations for major components of NVIDIA’s distributed data platform. Builds batch and streaming pipelines, data products, shared platform capabilities, quality standards, and operational practices using Python, SQL, Databricks, and Spark. Leads cross-team technical delivery, production investigations, architectural changes, mentoring, and adoption of reliable, secure, scalable data systems supporting GPU fleet health, capacity, utilization, cost, and operational decisions.

Build and operate NVIDIA DGX Cloud’s data platform, including batch and streaming pipelines, distributed data workloads, data products, quality monitoring, security, observability, and self-service consumption. Own systems from architecture through deployment and incident response. Improve platform reliability, scalability, and engineering practices while mentoring teammates. Work primarily with Python and SQL across cloud infrastructure, databases, Spark, telemetry, and operational data.