Senior Data Management Professional - Data Engineering - Commodities Data

Senior Data Management Professional - Data Engineering - Commodities Data

Location

Business Area

Data

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Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.

Our Team:

In the Commodities Data Team, we’re responsible for onboarding, modelling and maintaining data that are fit for purpose for our clients. More than 320,000 business leaders rely on the real time financial information available on the Bloomberg Professional Service. Our products run on intelligence and insight provided by the Commodities Team. Our team of analysts provide valuable data and insights to key decision makers within the commodity markets.

We are responsible for the data management of datasets across Power and Gas, Oil, Carbon, Agriculture and Metals. The team provides relevant, timely and accurate data to empower customers to drive their analysis of commodity markets, both pricing and fundamentals.

The Role:

The Commodities Data team is looking for a highly experienced Senior Data Management Professional to help lead the next generation of our data platform. This role requires a strong data engineering foundation combined with deep ownership of data quality, where quality is built directly into pipelines, systems and architecture rather than managed as a separate function. This role is designed for a top-tier individual contributor who thrives in complex environments and consistently delivers high-impact, scalable solutions.

You will be responsible for designing and evolving data systems that power Tier 1 datasets, improving reliability, reducing technical debt and modernizing legacy workflows. This includes building advanced ETL pipelines, implementing intelligent automation and developing robust data quality controls and monitoring frameworks to ensure data accuracy, completeness and timeliness.

In addition, you will play a key role in defining and executing the data quality vision for our datasets. This includes evolving fit-for-purpose quality metrics, understanding how clients consume data across Bloomberg products and aligning data with both client needs and Bloomberg’s commercial strategy. You will also influence data governance practices and lifecycle management across teams to ensure long-term data integrity and scalability.

You will collaborate closely with Product, Engineering and domain experts to define and execute on strategic data initiatives. In addition to hands‑on development, you will act as a technical leader within the team by owning end‑to‑end solutions, influencing architecture decisions and mentoring others.

We are looking for someone who operates at a high bar of technical excellence, takes ownership of both data systems and data quality outcomes, and leverages modern technologies including AI and machine learning to enhance data workflows and extract additional value from our datasets.

• 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 and validate improvements
• Build and deploy automated data quality controls, including anomaly…