Top Data Engineer Jobs in Portugal

Design, build, and maintain scalable data ingestion pipelines into BigQuery; replace legacy ETL/ELT; implement orchestration (Airflow/Dagster/Prefect); support DBT and CI/CD; tune BigQuery performance; set up monitoring/alerting; maintain documentation and data governance.

Support building and testing digital solutions for production lines: analyze operational workload, assist with big data analytics and prototypes, troubleshoot production/maintenance tasks, and communicate insights under mentorship.

As a Backend Software Engineer, you will design and implement scalable services, focusing on API design and system architecture while mentoring team members.

As a Software Engineer in Data, you will enhance core product experience, optimize integrations, manage data systems, and support scalable services.

The Lead Data Engineer will design and build data architectures, lead a team, and develop ETL processes using cloud technologies to optimize data solutions for the U.S. business.

Design and maintain scalable data pipelines, models, curated datasets, analytics solutions, and manufacturing automation tools. Support Ignition and Sepasoft MES systems, integrate manufacturing software, and develop solutions for metrology and image inspection data. Apply software engineering practices and independently lead complex cross-functional projects from requirements through deployment.

Develop and maintain production AI and software solutions for alternative asset management workflows. Embed with business teams to identify use cases, build self-service AI capabilities, and deliver agentic workflows, retrieval pipelines, and evaluation tools. Use Python, SQL, Git, CI/CD, Docker, Terraform, and cloud deployment practices. Transition proofs of concept into production, lead concurrent projects, train technical and business users, and collaborate with stakeholders to define requirements and scope.

Develop and maintain production AI and analytics solutions using Python and SQL. Partner with financial business units to identify workflow improvements, build agentic and retrieval-based AI applications, and transition proofs of concept into reliable deployments. Use Git, CI/CD, Docker, Terraform, and cloud technologies while establishing evaluation tools, prompt libraries, and runbooks. Lead multiple projects, train technical and business users, and communicate requirements, scope, documentation, and changes to stakeholders.

Build and maintain scalable data pipelines, analytics data warehouses, ETL processes, data models, and infrastructure supporting reporting, analytics, and machine learning. Ensure data quality from creation through presentation layers, translate stakeholder needs into deliverables, improve tooling and pipeline reliability, and participate in on-call support. The role involves modern big data architecture, containerization, cloud platforms, and data engineering technologies.

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.

Design, build, and maintain data structures, ETL/ELT pipelines, and data products. Support operational data processes, performance tuning, data governance, documentation, and stakeholder collaboration. Mentor junior staff and improve data platform quality and accessibility.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Design and operate high-throughput data pipelines, streaming services, time-series storage, and industrial data historians for sensor telemetry in test and production systems. Ensure reliable ingestion without data loss, define data architecture and schema standards, improve operational reliability, and support visualization and analytics teams. The role includes debugging, root cause analysis, engineering standards, and collaboration across controls, test automation, and visualization functions in safety-critical, 24/7 environments.

Build and operate reusable cloud data-platform infrastructure across engineering teams. Design, secure, monitor, optimize, and maintain relational, NoSQL, columnar, streaming, and analytical data systems. Establish database, schema, access, backup, recovery, privacy, and observability standards; improve infrastructure-as-code and operational tooling; and guide datastore selection and architecture. Lead cross-team initiatives, reduce technical debt, mentor senior engineers, document best practices, and influence technical strategy without direct management authority.

Lead technical data conversion for Oracle Fusion Cloud ERP implementations. Design source-to-target mappings, develop SQL and PL/SQL transformation processes, execute data load cycles using FBDI and HDL, and implement validation and reconciliation controls. Resolve complex migration issues, optimize performance, collaborate with functional consultants and client stakeholders, and mentor junior developers throughout the migration lifecycle.

Maintains and consolidates Airtable systems, troubleshoots API integrations across Salesforce, Coupa, and AWS finance tools, validates data flows, supports dashboards and executive reporting, and maintains data standards and documentation. Provides onsite operational support in New York while escalating complex issues to the Senior Data Operations Architect.

Build and enhance Databricks-based platform capabilities and reusable data products for quantitative researchers and analysts. Develop and operate pipelines integrating heterogeneous data sources, while improving governance, automation, observability, security, and operational excellence. Define target-state platform architecture, evaluate emerging technologies, and use infrastructure as code, CI/CD, Apache Airflow, and AI-assisted development tools to accelerate prototypes into production.

Design, build, maintain, and optimize data infrastructure (databases, data lakes, warehouses) and ETL/ELT pipelines. Ensure data quality, security, performance, and compliance. Collaborate with analysts and stakeholders, document policies, and support monitoring, backup, and recovery strategies.

Lead Pinterest’s technical strategy and roadmap for scalable big data and AI infrastructure. Build frameworks for compute, job, resource, scheduling, and shuffle management across petabyte-scale datasets. Partner with internal customers, provide company-wide technical leadership, and improve data processing reliability, speed, and efficiency.

Design and evolve core data models and durable APIs for complex communications networks. Build, test, operate, and maintain production-grade interfaces and abstractions for heterogeneous physical and logical networks while ensuring compatibility, consistency, quality, and performance. Manage project priorities, timelines, and deliverables within the Spacetime engineering team.

Design, evolve, and operate core data models and APIs for complex communications networks. Build durable abstractions for heterogeneous physical and logical networks, while maintaining interface quality, compatibility, consistency, and performance. Collaborate with experienced engineers to manage priorities, timelines, and deliverables across production systems. The role requires strong software engineering fundamentals and proficiency in Golang, C/C++, Rust, or Java.

Design, build, and operate highly scalable, multi-tenant cloud storage systems for workflow data. Responsibilities include developing persistence APIs, storage abstractions, replication paths, caches, tiered storage, and observability; improving reliability and performance through SLOs, chaos testing, profiling, and incident reviews; leading technical designs, mentoring engineers, and collaborating across teams.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Lead design, development, and operationalization of enterprise-scale data applications and pipelines. Build scalable ETL/ELT workflows for structured and unstructured data, drive data architecture and migration efforts, optimize Informatica ETL, work with relational and cloud data stores, support CI/CD and DevOps, and ensure monitored, reliable data platforms to enable analytics and mission outcomes.

Design, build, and maintain scalable data pipelines and platforms for analytics. Develop scripts to parse and convert structured and unstructured data, implement ETL/ELT workflows, collaborate with cross-functional teams, use version control, and support platform scaling, monitoring, and operations on mission-driven projects.