Data Engineer

Palantir Data Engineer Location: London, United Kingdom (Onsite) Role Overview We are seeking Data Engineers to join an established team focused on optimizing a mature Palantir Foundry environment. This role involves enhancing platform efficiency, improving reporting performance, and reducing compute costs. The position is a blend of data engineering, platform optimization, and FinOps, aimed at maximizing the value of an existing large-scale data platform that has been in place for approximately six years. Key Responsibilities

• Optimize and mature an existing Palantir Foundry implementation, focusing on data modeling, partitioning strategies, and storage design.
• Improve reporting performance, data refresh times, and compute efficiency for middle-office teams reviewing daily P&L data.
• Develop and maintain data pipelines, ingesting data from multiple trading systems and other sources like CSV files.
• Identify and rectify platform inefficiencies to improve performance and reduce costs associated with large datasets, often ranging from tens of millions to billions of records.
• Apply software development best practices, including code sharing, CI/CD, and maintainable development, primarily using Python and SQL.
• Collaborate with a team of data engineers to support ongoing engineering needs, project work, and platform enhancements. Required Qualifications Technical Skills: Proficiency in Python and SQL is required. Candidates must have strong object-oriented programming (OOP) and coding fundamentals. Experience: Experience in data engineering is required, specifically with large-scale data platforms and complex data pipelines. Candidates must have experience in the trading domain, with an understanding of concepts such as P&L reporting and exposure. Energy trading experience is preferred, but financial trading backgrounds are also acceptable. Preferred Qualifications
• Experience with Palantir Foundry.
• Familiarity with Stonebranch.
• Experience with commodities or energy trading systems.
• Knowledge of data ingestion optimization and pipeline performance tuning.
• Experience supporting very large-scale data environments.
• Familiarity with other object-oriented languages such as Java or C#.