Senior Data Management Professional - Data Quality - Commodities

Details

Bloomberg's Data organization delivers data, news, and analytics through innovative technology. This role focuses on improving the reliability, control environment, and operational efficiency of commodities and energy data through data quality solutions, automation, and collaboration with data operations, engineering, product, and business stakeholders.

The position is a senior individual contributor role requiring hands-on implementation, technical leadership, mentorship, and ownership of complex data quality problems from definition through implementation.

Responsibilities

• Design, implement, and enhance data quality controls across commodities datasets, including market data, reference data, and fundamentals.
• Build, maintain, and optimize automated data quality checks for completeness, accuracy, timeliness, consistency, and other quality dimensions.
• Develop scalable data quality approaches combining reusable controls and frameworks with domain-specific requirements.
• Define data quality metrics, thresholds, and monitoring approaches based on historical behavior, business context, and client impact.
• Monitor data quality metrics and controls, investigate exceptions, perform root-cause analysis, and drive issues through remediation and closure.
• Partner with data operations teams to identify recurring data issues and translate them into process improvements, automation, or engineering solutions.
• Improve DataOps processes by reducing manual intervention, standardizing workflows, strengthening controls, and identifying scalable automation opportunities.
• Partner with engineering and platform teams to improve observability, alerting, resiliency, and operational support for critical data pipelines.
• Develop and maintain automation solutions for data validation, analysis, exception handling, and workflow efficiency using Python, SQL, and other appropriate technologies.
• Lead technical solutions and improvements across the data lifecycle, including ingestion, normalization, enrichment, validation, and distribution.
• Ensure automated processes and controls are governed, transparent, maintainable, and aligned with business and control requirements.
• Identify opportunities to improve scalability, reduce operational risk, and address technical debt across data workflows.
• Act as a day-to-day partner for data operations, engineering, and business users on data quality and control topics.
• Provide technical leadership and mentorship in Python development, automation, data quality practices, and solution design.
• Establish technical standards and best practices and influence technical direction for data quality and automation initiatives.
• Evaluate and apply emerging technologies, including AI and machine learning, where they can improve data quality, automation, or operational efficiency.

Requirements

• 3+ years of experience in data management, data engineering, data quality, data operations, or a related technical discipline.
• Strong hands-on Python development skills, including experience building production-quality automation, data processing, validation, or analytical solutions.
• Strong practical experience with SQL or similar languages for data analysis, validation, and automation.
• Significant experience designing and implementing data quality controls, monitoring, and exception-management processes in complex data environments.
• Ability to independently investigate complex data issues, perform root-cause analysis, and design sustainable solutions.
• Experience with modern data platforms, workflow tools, or data observability and quality tooling.
• Experience owning complex technical initiatives end to end and driving them through implementation.
• Ability to translate business and data requirements into scalable technical solutions and fit-for-purpose data quality controls.
• Experience providing technical guidance, mentoring others, and influencing technical decisions or engineering practices.
• Strong organizational skills and the ability to manage multiple priorities through completion.
• Effective communication skills with technical and non-technical stakeholders.

Preferred Qualifications

• Experience with commodities, energy, market data, or trading-related datasets.
• STEM background or experience in technical, quantitative, or data-intensive disciplines.
• Familiarity with DataOps concepts and collaboration between data operations and engineering teams.
• Familiarity with statistical approaches to anomaly detection, dynamic thresholding, or time-series data quality monitoring.
• Experience in a regulated or controlled data environment.
• Exposure to cloud-based data platforms and pipeline monitoring tools.
• Experience supporting automation, controls, or AI/ML-based data solutions within a defined validation framework.

Benefits

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