Top Data Engineer Jobs in Lisbon

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Design and build data pipelines and digital process twins from systems like SAP and Salesforce, perform process mining analyses to find bottlenecks, produce business cases, create ETL/data models for dashboards, collaborate on AI/RPA improvements, and present and support implementation of process changes.

Design and implement scalable data platforms and multi-agent pipelines on cloud infrastructure. Enable self-service ingestion, ELT automation, data-as-a-product, and LLM-augmented features (RAG, semantic search). Partner with analysts and data scientists, ensure data protection, and drive platform roadmap and engineering best practices.

Lead data governance for the SIXT Data Platform: design access controls and row-level security, onboard domains into DataHub, enforce metadata and KPI standards, define data ownership and quality monitoring, support BI platform modernization, and enable AI-driven governance and data literacy.

The Staff Data Engineer will manage product analytics architecture, drive data modeling standards, design data systems, and provide technical leadership across teams to improve data insights and product capabilities.

The Senior Data Engineer will develop and maintain data pipelines, support data analysis, ensure architecture meets business needs, and communicate with stakeholders.

The Data Engineer role focuses on programming, data integration, and working within cloud environments while ensuring team collaboration and professional development.

Design, build, and maintain Snowflake and dbt pipelines for the US revenue data chain (Billing → ARR → Deferred Revenue → Rev Rec → Close Pack). Deliver tested, documented ingestion and transformation models, ensure pipeline reliability during financial close (on-call), set data quality and governance standards, enable AI and finance consumers, and extend models to OPEX/COGS/commissions as scope grows.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

As a Data Engineer, you will analyze complex data sets, collaborate with teams, perform ETL processes, and contribute to data model design and reporting.

The role involves designing and migrating data processes from SQL Server to GCP, developing ETL pipelines, optimizing databases, and collaborating with teams to ensure smooth transitions and data integrity.

The Junior Data Engineer will design and optimize data infrastructure on GCP, focusing on ETL/ELT pipelines and ensuring data quality, governance, and security.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Build and maintain cloud and on-prem data pipelines, monitor and resolve batch failures, engage stakeholders to translate business needs into data solutions, deliver projects end-to-end, coach junior teammates, and define best practices for the UK data platform.

Build, maintain, and scale ELT data pipelines and dbt models in BigQuery; write Python for data processing; ensure data quality and collaborate with analytics, product, and game teams.

Design and build scalable Java/Spring Boot microservices for high-volume data ingestion, processing, and access. Work with SQL and data pipelines, integrate cloud and legacy systems, optimize performance, write tests, participate in architecture and CI/CD, and deploy containerized services using Kubernetes and Helm while contributing to platform modernization.

Lead and scale the Data Engineering function, overseeing data platform modernization, integration architecture, data warehousing strategy, and team growth while ensuring reliability, compliance, and optimized pipelines for business impact.

As a Principal Software Engineer, you will design and implement automated testing frameworks and maintain test infrastructure to ensure system reliability and correctness, focusing on collaboration and cross-functional improvements.

The Software Engineer will support data collection for model training, manage ingestion pipelines on GCP, and collaborate with data scientists to enhance dataset quality and scale.

Build and maintain real-time streaming data pipelines and feature stores to power low-latency model inference. Collaborate with ML and AI infra teams on data contracts, observability, and SLAs; drive batch-to-real-time migrations and evaluate emerging streaming/feature store technologies.

Design, build, and own streaming data pipelines and feature stores to enable low-latency inference and real-time model serving. Partner with ML and infra teams to define data contracts, ensure data quality, observability, and SLAs, and move batch pipelines toward real-time production. Evaluate streaming and feature store technologies and contribute to inference pipeline tooling.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.