Senior Full-Stack Data Engineer

Work at the intersection of Data Engineering, Platform Engineering and Quantitative Analytics within the energy trading domain. Build and productionize data solutions using Azure, Databricks, Python, SQL and Spark across batch and streaming workloads. Help transform analytical prototypes into scalable, reliable and maintainable data products while supporting the wider data platform migration.

Act as an embedded engineer within a business-facing analytics and quantitative team. Lead and support the team through ongoing data platform migration initiatives. Design and maintain reliable Azure-based data pipelines and data products. Convert analyst-developed Python notebooks into tested, maintainable production solutions. Deploy, monitor and support Azure Databricks workloads in production. Work closely with analysts and quantitative specialists to understand business objectives and translate them into scalable technical solutions. Help professionalize the development of lifecycle through testing, CI/CD, automation, monitoring and documentation. Optimize data transformations, models, and reporting workloads. Provide technical guidance and help colleagues with varying levels of engineering maturity adopt sustainable engineering practices.

As a Senior Data Engineer, you’ll work as an embedded engineer within a business-facing analytics and quantitative team. You’ll help develop and maintain reliable data solutions while supporting the team through its data platform migration and strengthening engineering practices. Design and maintain reliable Azure-based data pipelines and data products. Deploy, monitor and support Azure Databricks workloads in production. Turn analyst-developed Python notebooks and analytical prototypes into tested, maintainable production solutions. Work closely with analysts and quantitative specialists to translate business objectives into scalable technical solutions. Build and support batch and streaming data pipelines. Help improve the development lifecycle through testing, CI/CD, automation, monitoring and documentation. Optimize data transformations, models and reporting workloads. Provide technical guidance and help colleagues adopt sustainable engineering practices. Lead and support the team through ongoing data platform migration initiatives.

You have: A strong Data Engineering background in Azure cloud environments. Hands-on Azure Databricks experience, including Spark, Delta Lake, Workflows and Unity Catalog. Strong Python and SQL skills. Experience building batch and streaming data pipelines. Experience turning notebooks and analytical prototypes into production-grade solutions. Knowledge of testing, code reviews, CI/CD and software engineering best practices. Experience working directly with analysts, data scientists, quantitative specialists or business-oriented teams. A strong ability to balance business understanding with technical execution. Experience with Azure services such as ADLS Gen2, Azure DevOps, Key Vault, Data Factory or equivalent. Experience contributing to platform migration or modernization initiatives. Nice to have: Machine Learning Engineering or MLOps experience. Terraform or Bicep. Azure monitoring and observability tooling. DataOps practices and orchestration frameworks. Dashboard performance optimization and semantic modeling.

You’ll be part of a business-facing team within the energy trading domain, working at the intersection of Data Engineering, Platform Engineering and Quantitative Analytics. You’ll work closely with analysts and quantitative specialists to understand business objectives and translate them into scalable technical solutions. The environment is built around Azure and Azure Databricks, with the team developing and maintaining data pipelines and data products while progressing through an ongoing data platform migration. An important part of the work is strengthening engineering practices across testing, CI/CD, automation, monitoring and documentation.