Senior Data Management Professional - Data Engineering - Commodities Data

Details

Bloomberg's Commodities Data Team is responsible for onboarding, modeling, and maintaining fit-for-purpose data across Power and Gas, Oil, Carbon, Agriculture, and Metals. The team delivers relevant, timely, and accurate data to support analysis of commodity markets, including pricing and fundamentals.

This role will help lead the next generation of Bloomberg's data platform. It combines data engineering with ownership of data quality, building quality directly into pipelines, systems, and architecture. The position involves designing and evolving data systems for Tier 1 datasets, improving reliability, reducing technical debt, modernizing legacy workflows, building advanced ETL pipelines, implementing intelligent automation, and developing data quality controls and monitoring frameworks.

The role also involves defining data quality metrics, understanding how clients consume data across Bloomberg products, influencing data governance and lifecycle management, collaborating with Product, Engineering, and domain experts, owning end-to-end solutions, influencing architecture decisions, and mentoring team members.

Responsibilities

• Build and maintain scalable, resilient, and observable data pipelines supporting critical Commodities datasets.
• Lead the modernization of legacy workflows to reduce technical debt and improve maintainability and performance.
• Perform data profiling, deep data analysis, and root cause analysis to support data-driven decisions and validate improvements.
• Build and deploy automated data quality controls, anomaly detection, proactive monitoring, alerting, and data reliability frameworks.
• Apply AI and machine learning techniques, including natural language processing, entity extraction, classification, anomaly detection, and LLM-assisted workflows, to improve data ingestion and enrichment.
• Identify opportunities to use generative AI and automation to reduce manual workflows and accelerate data onboarding.
• Develop validation frameworks for agentic artifacts to ensure quality, reliability, and appropriate controls.
• Own and drive large-scale data migrations and system redesigns.
• Establish best practices for data architecture, pipeline design, and workflow orchestration.
• Translate client data-consumption needs into measurable data quality and product improvements.
• Partner with Engineering on platform evolution, scalability, and system design.
• Act as a technical leader and mentor, raising standards for code quality, design thinking, and execution.
• Apply project management expertise to keep technical projects aligned with requirements and on track.

Requirements

• Bachelor's degree or higher in Statistics, Computer Science, Quantitative Finance, or another STEM-related field, or equivalent qualifications.
• At least 4 years of experience architecting, designing, and implementing scalable data solutions and ETL pipelines, including monitoring, remediation, and data management workflows across diverse data sources.
• At least 4 years of hands-on Python experience in development and production environments.
• Experience working with SQL and/or NoSQL databases.
• Proven experience 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, such as classification, NLP, anomaly detection, or LLM-assisted workflows.
• Strong data quality management experience, including defining metrics, performing root cause analysis, and driving measurable improvements in data reliability.
• Experience building observable systems with monitoring, alerting, and data reliability frameworks.
• Ability to analyze and refactor legacy systems and improve performance and scalability.
• Familiarity with databases, schemas, data modeling, and structured and unstructured formats such as PDF, HTML, XBRL, JSON, and CSV.
• Strong communication and interpersonal skills, including the ability to influence technical direction, mentor team members, communicate complex concepts, and collaborate across diverse and distributed teams.

Preferred Qualifications

• Advanced degree in a relevant subject and/or Certified Data Management Professional certification (CDMP), or progress toward certification.
• Experience with Bloomberg products, Bloomberg Terminal fluency, and/or Bloomberg Data Workflows.
• Experience with commodities markets and products.
• Experience productionizing AI or machine learning models within data platforms.
• A record of driving efficiency gains through automation and intelligent systems.
• Strong understanding of data governance, lineage, and metadata management at scale.
• Hands-on project management experience and familiarity with JIRA and QlikSense.

Benefits

Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental and vision coverage, short- and long-term disability benefits, a 401(k) match, life insurance, and wellness programs.