FP&A Data Engineer, Galaxy

• A candidate should have experience developing Python/SQL-based solutions, and a detailed working knowledge of core data engineering concepts (relational and non-relational data structures, ETL processes, unit/integration testing)
• You should be able to conceptualize the “big picture” and translate concepts into detailed tactical execution plans while maintaining a high degree of accuracy
• This person should be comfortable working in a fast-paced environment and have the ability and “grit” necessary to manage multiple high priority tasks
• 6+ years of progressive data engineering experience with deep expertise in Python and SQL
• Familiarity of various asset classes (FX, futures, options, etc.) and how to model their economic impact on a firm (PnL and capital)
• Strong analytical skills and experience working with large, unstructured datasets including data pipeline development to support insight generation
• Intellectually curious self-starter, comfortable in ambiguity and a fast-paced environment
• Highly detailed oriented with excellent communication skills

Desirable

• Working as part of the FP&A team, you will be on the frontlines of maintaining and developing data pipelines, financial models and automated reporting covering our various business units – with a focus on trading and protocol services
• Deliveries will directly feed management reporting whose audience includes individual traders/teams and company executives
• Architect and own the FP&A insights data warehouse, establishing the single source of truth for firmwide management reporting, PnL analytics, budgeting, KPI tracking, and other strategic business intelligence
• Leverage Python and SQL to build and evolve complex PnL attribution and KPI generation models to drive management reporting across multiple reporting cadences (daily, weekly, quarterly)
• Implement automated reconciliation frameworks ensuring data integrity across FP&A reporting systems, reducing manual processes and operational risk
• Manage cross-functional initiatives partnering with Technology teams to modernize legacy FP&A Excel and Python models, defining technical requirements and architectural standards
• Integrate best practices in data governance, version control, and testing across all FP&A technical development workflows
• Provide easily accessible data for wider team, to support ad-hoc analyses for the business and CFO’s organization