Senior Data Management Professional - Data Engineer - Commodities Data

Location: Princeton Business Area Data Ref # 10053991

About the role

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 innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes.

Responsibilities

• Design, build, and maintain scalable, resilient data pipelines and workflows supporting critical commodities datasets.
• Develop robust data processing and automation solutions using Python, SQL, and other appropriate technologies.
• Own complex technical initiatives end-to-end, from requirements and solution design through implementation, testing, deployment, and ongoing support.
• Modernize legacy data workflows, reducing technical debt, manual intervention, and operational risk while improving maintainability and performance.
• Design solutions that can be reused and scaled across datasets and workflows rather than solving similar problems independently.
• Work across the data lifecycle, including acquisition, ingestion, transformation, normalization, enrichment, validation, storage, and distribution.
• Partner with Engineering and platform teams on architecture, workflow orchestration, observability, resiliency, and the evolution of our data platforms.
• Establish and promote technical standards and best practices around Python development, pipeline design, testing, automation, and maintainability.
• Investigate complex data and production issues, perform root-cause analysis, and implement sustainable solutions that prevent recurrence.
• Build appropriate validation, monitoring, and data quality controls into data pipelines to ensure reliable and fit-for-purpose data.
• Identify opportunities to improve scalability and operational efficiency through automation and better technical design.
• Understand how clients consume commodities data and translate business and product requirements into effective technical solutions.
• Partner with stakeholders across Data, Engineering, and Product to define requirements, evaluate tradeoffs, and drive technical initiatives through delivery.
• Provide technical leadership and mentorship to team members, helping develop their Python, data engineering, automation, and solution-design capabilities.
• Influence technical direction by evaluating approaches, challenging existing designs where appropriate, and helping the team make sound long-term architectural decisions.
• Evaluate and apply emerging technologies, including AI and machine learning, where they can meaningfully improve data acquisition, processing, automation, or operational efficiency.