About Bloomberg Data: Bloomberg runs on data. Our products are powered by rich, timely, and highly contextualized information. Within the Data department, we are responsible for acquiring, transforming, and delivering trusted data that fuels Bloomberg's products and analytics. We work at the intersection of scale, complexity, and mission-critical reliability.

The Team: The Entities Data Management Team owns the core entity data that underpins Bloomberg’s financial products, including corporate hierarchies, risk attribution, and issuer relationships across public and private markets. We’re modernizing how this data is sourced, processed, and governed—ingesting from structured third-party feeds, unstructured documents and internal systems.
We are building scalable, automated pipelines and robust governance frameworks to handle hundreds of millions of records with the accuracy and transparency that our clients expect. As part of this effort, we are implementing new architecture for ingesting and reconciling diverse data inputs with clear lineage, observability, and quality metrics.

The Role: We are looking for a Senior Data Automation Engineer who operates at the intersection of data engineering and data product strategy. You’ll take ownership of building our decision engine—the core arbitration logic that evaluates competing inputs across multiple sources to determine the most accurate, complete, and timely data point for publishing.
This role requires someone with a deep understanding of entity and reference data, as well as the technical acumen to design and operate data pipelines at massive scale. You’ll be expected to profile datasets, evaluate quality and consistency, and improve processing workflows with a strong focus on data lineage, observability, and governance. You will collaborate closely with Product Managers, Engineering, and cross-functional data teams to ensure our platform is extensible, transparent, and aligned to business and client needs.

You Will: - Design and build the data arbitration and decision engine to resolve conflicts across multiple data sources, determining which values to publish.

• Drive the standardization and automation of our ingestion pipelines across structured, unstructured, and internal sources.
• Conduct data profiling and analysis to identify quality gaps, inconsistencies, and opportunities for process improvement.
• Implement data lineage, observability, and monitoring frameworks to ensure transparency, traceability, and reliability.
• Collaborate with Engineering and Product to define and evolve platform requirements and technical architecture.
• Apply a data product mindset—balancing engineering efficiency with data quality, client needs, and long-term maintainability.
• Support the integration of AI/LLM-based tools as part of our larger data processing and enrichment strategy.

**You’ll Need to Have: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.- 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 (e.g., Python, Java, Scala) and modern data tooling (e.g., Spark, Airflow, Kafka).
• 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, data normalization, 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 with varying levels of technical knowledge.
• Experience building decision engines using rules-based logic and/or AI/ML or LLM-based models

We’d Love to See: - Familiarity with frameworks like DCAM or DAMA-DMBOK.

• Experience working in AWS and/or Azure for cloud-native data processing and storage
• Proficiency with Git and CI/CD pipelines for reliable, production-grade deployments
• Familiarity with cloud data services (e.g., S3, EMR, Glue, ADLS, Data Factory, Databricks)
• Experience implementing data observability tools (e.g., Monte Carlo, Open Lineage, or custom solutions).

**Why Join Us?**This is a unique opportunity to shape the architecture and decision logic of one of Bloomberg’s most foundational data sets. You’ll have the chance to influence how we ingest, arbitrate, and publish data at scale—driving transparency, quality, and innovation across our platform. If you thrive at the intersection of data engineering and product strategy, and you’re passionate about solving big data problems with impact, we’d love to hear from you.

**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!