The Senior Data Management Professional role in the Commodities Data Team focuses on leading the development of a next-generation data platform. This position requires a strong foundation in data engineering and emphasizes the importance of data quality integrated into systems and architecture. The team is responsible for managing datasets across various commodities, providing timely and accurate data to clients. The role involves collaboration with product and engineering teams to enhance data workflows and implement innovative solutions.

• A bachelor’s degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree-equivalent qualifications.
• 4+ years of experience architecting, designing and implementing scalable data solutions and ETL pipelines.
• 4+ years of hands-on experience working with Python in development/production environments and working with databases either SQL/NoSQL.
• Proven track record of owning and delivering complex, high-impact data initiatives end-to-end.
• Strong experience with distributed data systems, workflow orchestration and scalable architecture design.
• Hands-on experience applying machine learning or AI techniques in data workflows.
• Strong experience in data quality management, including defining metrics and performing root cause analysis.
• Experience building observable systems with monitoring, alerting and data reliability frameworks.
• Ability to analyze and refactor legacy systems and drive measurable improvements in performance and scalability.
• Strong communication and interpersonal skills.

• Build and maintain highly scalable, resilient and observable data pipelines supporting critical Commodities datasets.
• Lead the modernization of legacy workflows, reducing technical debt and improving maintainability and performance.
• Perform deep data analysis including profiling and root cause analysis to support data-driven decision making.
• Build and deploy automated data quality controls, including anomaly detection and proactive monitoring.
• Apply AI and machine learning techniques to improve data ingestion and enrichment.
• Identify opportunities to leverage generative AI and automation to reduce manual workflows.
• Develop validation frameworks for agentic artifacts to ensure quality and reliability.
• Own and drive large-scale data migrations and system redesigns.
• Establish best practices around data architecture, pipeline design and workflow orchestration.
• Partner with Engineering to align on platform evolution, scalability and system design.

• Bloomberg offers a comprehensive suite of benefits designed to support health, financial stability, and work-life balance.