Top Data Engineer Jobs in Portugal

Build and maintain scalable data platforms and systems that transform raw market data into validated analytical datasets, including bars, residuals, curves, volatility surfaces, and greeks. Collaborate with trading, research, and external vendor teams to understand datasets and workflows, implement calculations, document technical solutions, and ensure reliable delivery through strong testing and validation practices.

Develop and implement data pipelines and large-scale datasets for reporting and analytics. Analyze data sources for quality and availability, standardize data structures, document technical designs, create unit tests, and support QA testing. The internship provides supervised, practical data engineering experience, mentorship, leadership exposure, and opportunities to present project outcomes.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Design rigorous experiments, train and evaluate models, investigate synthetic and agentic data generation, and translate findings into scalable products. Collaborate with research, engineering, product, and operations teams, communicate technical insights, contribute to publications and open-source work, and mentor peers. This is a fully onsite role based in San Francisco, Palo Alto, or Seattle.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Build and lead scalable data governance systems across the data platform, including privacy policy enforcement, metadata and lineage management, data security, regulatory compliance, and purpose-driven access. Architect reliable systems for high-volume data environments, shape technical strategy and roadmaps, collaborate with Privacy, Legal, Security, and Trust teams, and mentor engineers across backend infrastructure and data engineering.

Designs, builds, and maintains scalable data pipelines, warehouses, and lakes integrating complex network operations data. Uses Kafka, Elastic Stack, AWS, OpenShift, Kubernetes, and automation to support analytics, reporting, and operational dashboards. Responsibilities include ETL/ELT, data quality, performance optimization, CI/CD, security compliance, troubleshooting, and documentation. Collaborates with analysts and visualization engineers in an Agile federal IT modernization environment.

Leads the architecture, engineering, and delivery of enterprise data platforms and analytics solutions for clients. Advises executive and technical stakeholders, facilitates discovery and architecture sessions, designs cloud-based batch and streaming systems, and remains hands-on with pipelines, code reviews, proofs of concept, and complex technical issues. Establishes DataOps, governance, security, and engineering standards; mentors data teams; leads distributed delivery teams; and supports estimation, proposals, presentations, and pre-sales activities.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Build and maintain centralized, validated data platforms covering reference, fundamental, market, index, exchange, instrument, and security master datasets. Analyze vendor data, design scalable data models, implement quality controls, resolve processing issues, and partner with quantitative researchers and traders. Provide technical leadership and mentorship while maintaining testing and system design standards across the Core Data team.

Build and maintain the Air Force Data Mesh Environment through scalable ETL and ELT pipelines, data models, orchestration workflows, APIs, and governed data applications. Develop secure, maintainable software using Python, TypeScript, React, Node.js, SQL, and modern data tools. Collaborate with product, DevOps, security, and mission stakeholders while applying testing, validation, observability, and secure data-handling practices.

Build data pipelines and machine learning workflows for Cisco products. Develop, train, deploy, and scale models using Python and ML frameworks such as PyTorch or scikit-learn. Work with distributed processing and orchestration tools including Spark and Kubernetes while supporting performant, reliable AI data systems. The 12-week internship includes technical learning, cross-functional collaboration, networking, and exposure to product and platform development.

Provides technical leadership for Finance, Actuarial, and Corporate Data platforms. Defines engineering strategy and standards, architects automated data pipelines, leads cloud migrations and ETL modernization, and drives AI-assisted development, DevOps, CI/CD, testing, and data quality practices. Manages senior data engineers and distributed teams, resolves complex engineering issues, governs technical delivery, and communicates strategies to executive stakeholders. Requires extensive enterprise data engineering, cloud platform, data warehousing, automation, and people leadership experience.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Build and maintain scalable ETL/ELT pipelines and data workflows using Python, PySpark, and SQL. Improve data quality, reliability, architecture, and governance while supporting analytics, reporting, and machine learning initiatives. Collaborate with data scientists, analysts, and business stakeholders, use Azure DevOps for CI/CD and release management, and mentor junior engineers. The role also involves API integration, cloud data platforms, testing, version control, and contributing to engineering best practices.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Responsibilities include pipeline monitoring, incident troubleshooting, performance and cost optimization, design reviews, documentation, and knowledge sharing. The role requires strong SQL and Python, data modeling, orchestration, CI/CD, cloud data warehouse, security, governance, and healthcare data knowledge.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, monitor pipelines, troubleshoot incidents, optimize data platform performance and costs, and document scalable data engineering practices. Collaborate with analysts, data scientists, software engineers, and other stakeholders while supporting healthcare data configurations and privacy requirements.

Develops and optimizes scalable ETL/ELT data pipelines and analytics solutions for DoD mission requirements. Processes diverse large-scale datasets, maintains pipeline operations, monitors system health, optimizes databases and costs, prototypes data capabilities, and collaborates with technical teams. Implements DoD STIGs and secure data retrieval methods across enterprise and potentially classified environments.

Lead the architecture and implementation of Shield AI’s knowledge-graph-centered data platform. Build production APIs, DataOps infrastructure, storage and compute integrations, ingestion paths, SDKs, and reference architectures. Establish data-modeling, schema evolution, lineage, observability, security, reliability, and lifecycle standards. Partner with autonomy, ML, testing, infrastructure, product, and customer teams while evaluating technologies and remaining hands-on with implementation, benchmarking, and debugging across cloud and customer-managed environments.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Analyze manufacturing and spacecraft telemetry to identify anomalies, test failures, and degradation patterns. Build predictive models, SQL queries, operational dashboards, statistical process controls, and visualizations using Python and related tools. Document reproducible analyses in Jupyter notebooks and communicate findings to engineering, manufacturing, and mission operations teams. This is a three-month temporary, potentially convertible role supporting hardware production and spacecraft operations.

Lead Data SRE responsible for ensuring reliability, scalability, and performance of data platforms. Design monitoring, automate operations, optimize performance, respond to incidents, plan capacity, enforce security/compliance, document systems, and collaborate with engineering, data science, and product teams.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Leads the design, development, testing, and optimization of large-scale data engineering solutions. Responsibilities include building Python and PySpark ETL pipelines, orchestrating workflows, developing AWS cloud-native services, managing data lakes and Snowflake environments, and implementing testing, CI/CD, and secure engineering practices. The role also promotes responsible AI-assisted development, mentors engineers, participates in code reviews, and drives technical delivery initiatives.

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.