Senior Data Management Professional - Data Quality - Entities Data (Private Markets)

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 optimize the value of our data by combining domain and technical expertise to make data fit-for-purpose, timely, and accurate. We apply our problem-solving skills to identify innovative workflow efficiencies and deliver a high level of customer service. Our Team The Entity Data team is responsible for the creation, enrichment, and support of core entity data, including legal entities, issuers, and corporate hierarchies, for over 7 million public and private companies. This data underpins foundational reference data, private markets, and risk management. The team partners closely with Engineering, Product, and other Data groups to ensure our data management processes, ingestion pipelines, quality controls, and delivery methods evolve with market needs. What’s the Role? As a Data Quality Manager, you will define and execute the quality strategy that ensures our datasets are reliable, consistent, and ready for critical client use cases. You will lead efforts to design and implement effective data management practices, establish quality metrics, and deliver transparent reporting. Your work will directly support the transformation of our data workflows, enabling scalability for private markets and beyond. This role requires a mix of technical expertise, governance mindset, and stakeholder engagement skills. You’ll collaborate closely with Engineering, Product, and internal Centers of Excellence to ensure our datasets evolve in line with industry standards while also meeting client expectations for accuracy, coverage, and usability. We’ll Trust You To: Develop and deliver the data quality strategy for the Entities product, aligning it with client use cases and industry best practices. Define, measure, and monitor data quality metrics that ensure transparency and accountability across workflows. Partner with Product, Engineering, and Data teams to embed quality-by-design principles into ingestion, transformation, and delivery pipelines. Perform data profiling, statistical analysis, and root cause investigations to validate approaches and recommend improvements. Lead initiatives to modernize workflows—advocating for automation, AI/ML-enabled quality checks, and scalable infrastructure enhancements. Promote data quality awareness and education across the organization, empowering colleagues to embed quality into their work. Stay ahead of industry developments in reference data and private markets to shape our data strategy.

*Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role. 3+ years of professional experience in data science, data quality management, reference data, or data governance Excellent project management skills and the ability to collaborate across teams and geographies Strong communication skills to influence stakeholders as well as present complex findings clearly to both technical and non-technical audiences Strong coding skills in Python (e.g. OOP, PySpark, Pandas, NumPy, etc) and experience with SQL Strong statistical & analytical skills (e.g. sampling methods), attention to detail, and a proactive empirical mindset Familiarity with data modelling & modern data tech stack tools such as Airflow, dbt, Kafka, Iceberg, Trino, Superset etc. Familiarity with some data visualization tools (Tableau, QlikSense, Power BI, or similar) to communicate quality insights Familiarity with at least one version control system (e.g. Git) and collaborative development platform (e.g., GitHub, GitLab) Familiarity with ETL processes, data pipelines, workflow & schema design Comfort working in a dynamic, evolving environment, balancing long-term strategy with immediate operational needs We’d love to see: Industry-recognized data management certifications (e.g., DAMA, CDMP, DCAM). Experience with reference data, entity/issuer data, or private markets datasets, including integration of third-party providers. Exposure to Agile methodologies such as Scrum or Kanban. Hands-on knowledge of data governance and quality frameworks, including metadata management and regulatory considerations. Familiarity with modern data infrastructure and architecture (APIs, pipelines, cloud platforms), with exposure to AI/ML or LLM-based enrichment solutions for anomaly detection and automation. Exposure to Agile methodologies such as Scrum or Kanban. Awareness of emerging trends in Private Markets data, including the complexities of non-public entities and their role in financial workflows. Understanding of financial use cases that depend on accurate entity data, such as client onboarding, compliance/KYC, counterparty risk, and issuer classification. Does this sound like you? Apply if you think we're a good match. We'll get in touch to let you know what the next steps are! If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.