Senior Data Management Professional - Data Quality - Commodities

Senior Data Management Professional - Data Quality - Commodities Location Princeton Business Area Data Ref # 10053765 Description & Requirements 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.What’s the role?We are seeking a highly experienced, hands-on Data Quality and automation professional to help drive the reliability, control environment, and operational efficiency of commodities and energy data. This role will focus on designing and evolving data quality solutions, driving automation initiatives, and partnering closely with data operations, engineering, product, and business stakeholders to improve critical data pipelines and resolve complex data quality challenges.This is a senior individual contributor role suited to someone with a strong technical background who can combine hands-on implementation with broader technical leadership. You will be expected to take ownership of complex data quality problems from definition through implementation, help determine the right controls and technical approaches, and influence how the team builds scalable and sustainable solutions.In addition to delivering solutions directly, you will provide technical guidance and mentorship to others, help establish best practices, and influence technical direction across the team.We’ll trust you to:Design, implement, and continuously 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 fit-for-purpose quality dimensions.Develop scalable approaches to data quality that combine reusable controls and frameworks with domain-specific requirements.Define and evolve meaningful 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.Work closely with data operations teams to identify recurring data issues and translate them into sustainable process improvements, automation, or engineering solutions. Improve DataOps processes by reducing manual intervention, standardizing workflows, strengthening controls, and identifying opportunities for scalable automation.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 well 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 key day-to-day partner for data operations, engineering, and business users on data quality and control topics.Provide technical leadership and mentorship to team members, helping raise the bar for Python development, automation, data quality practices, and solution design. Help 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 meaningfully improve data quality, automation, or operational efficiency. You’ll need to have:3+ years experience in data management, data engineering, data quality, data operations, or a related technical discipline.Strong hands-on Python development skills, with 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. Proven ability to independently investigate complex data issues, perform root-cause analysis, and design sustainable solutions rather than relying solely on manual remediation. Experience working with modern data platforms, workflow tools, or data observability / quality tooling.Demonstrated 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, with the ability to manage multiple priorities and drive work through to completion.Effective communicator with the ability to work across technical and non-technical stakeholders.We’d love to see:Experience with commodities, energy, market data, or trading-related datasets.STEM background or experience working with technical, quantitative, or data-intensive disciplines.Familiarity with DataOps concepts and how data operations and engineering teams work together to improve reliability and delivery.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 implementation of automation, controls, or AI/ML-based data solutions within a defined validation framework.Salary Range = 110,000 - 190,000 USD Annual + Benefits + BonusThe referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level. We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.