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

Overview

In this role, you define and execute the data quality strategy for the Entity Data product, ensuring datasets are reliable and fit-for-purpose. You will work with Engineering, Product, and data teams to embed quality principles across ingestion, transformation, and delivery pipelines. Your work enables scalable private markets data and supports client-critical use cases with transparent metrics and reporting. You will drive data governance, tooling improvements, and awareness to embed quality across the organization. This is a hands-on role that combines technical rigor with stakeholder collaboration to shape Bloomberg’s reference data quality at scale.

Responsibilities

• Develop and deliver the data quality strategy for the Entities product aligned with client use cases and best practices
• Define, measure, and monitor data quality metrics for transparency and accountability
• Collaborate with Product, Engineering, and Data teams to embed quality-by-design in ingestion, transformation, and delivery pipelines
• Perform data profiling, statistical analysis, and root cause investigations to validate approaches and propose improvements
• Lead modernization of workflows with automation, AI/ML-enabled quality checks, and scalable infrastructure
• Promote data quality awareness and education across the organization
• Stay ahead of industry developments in reference data and private markets to inform the data strategy

Key requirements

• 3+ years in data science, data quality management, reference data, or data governance
• Excellent project management and cross-team collaboration across geographies
• Strong communication to influence stakeholders and present findings
• Strong Python coding (OOP, PySpark, Pandas, NumPy) and SQL
• Strong statistical and analytical skills with attention to detail
• Familiarity with data modeling and modern data tools (Airflow, dbt, Kafka, Iceberg, Trino, Superset)
• Familiarity with data visualization tools (Tableau, QlikSense, Power BI)
• Familiarity with version control (Git) and collaboration platforms (GitHub, GitLab)
• Familiarity with ETL, data pipelines, workflow & schema design
• Comfort in a dynamic environment balancing long-term strategy with operational needs

• strong communication
• stakeholder engagement
• collaboration across teams and geographies
• Python (OOP, PySpark, Pandas, NumPy)
• SQL
• Airflow

…