The Senior Data Automation Engineer will work within the Entities Data Management Team at Bloomberg, focusing on building a decision engine that evaluates competing data inputs to ensure accuracy and timeliness. This role combines data engineering with product strategy, requiring a deep understanding of entity and reference data. The team is modernizing data sourcing and governance, aiming to handle vast amounts of records with high reliability. The position involves collaboration with various teams to enhance data processing workflows and implement robust governance frameworks.

• 4+ years of experience in data engineering, data architecture, or data automation roles.
• Experience working with financial data, especially within reference or entity/company data domains.
• Strong proficiency in a programming language such as Python, Java, or Scala.
• Strong SQL skills for data transformation, validation, and reconciliation.
• Demonstrated experience working with large-scale datasets, ideally in domains such as reference or entity data.
• Experience with multi-source data arbitration and resolving conflicts across heterogeneous datasets.
• Deep understanding of data governance, quality frameworks, and metadata management.
• Strong analytical mindset and experience with data profiling and validation techniques.
• Proven ability to work independently and cross-functionally in a fast-evolving environment.
• Excellent communication skills and the ability to explain technical decisions to stakeholders.

• Design and build the data arbitration and decision engine to resolve conflicts across multiple data sources.
• Drive the standardization and automation of ingestion pipelines across structured, unstructured, and internal sources.
• Conduct data profiling and analysis to identify quality gaps and opportunities for process improvement.
• Implement data lineage, observability, and monitoring frameworks to ensure transparency and reliability.
• Collaborate with Engineering and Product to define and evolve platform requirements and technical architecture.
• Apply a data product mindset to balance engineering efficiency with data quality and client needs.
• Support the integration of AI/LLM-based tools as part of the data processing and enrichment strategy.

• Bloomberg offers a comprehensive suite of benefits designed to support health, financial stability, and work-life balance.