Senior Data Management Professional - Data Engineering - Entities

Details

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 workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.

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. The team is modernizing how this data is sourced, extracted, processed, and governed—especially from company filings, annual reports, regulatory disclosures, third-party documents, unstructured content, and internal systems.

They are building scalable, automated pipelines and human-in-the-loop workflows to ingest and transform data from high-value documents with accuracy, transparency, and governance. This includes new architecture for document-driven data acquisition, automated extraction, validation, lineage, observability, and quality measurement.

Role overview

This role is for a Senior Data Automation Engineer operating at the intersection of data engineering, document intelligence, and data product strategy. You will design and build automated ingestion pipelines that extract, normalize, validate, and prepare entity data from company filings, annual reports, regulatory documents, and other structured and unstructured sources. The role emphasizes profiling source documents and extracted datasets, evaluating quality and consistency, and improving automation workflows with a focus on data lineage, observability, governance, and human-in-the-loop oversight. You will collaborate with Product Managers, Engineering, and cross-functional data teams and help shape how AI, LLMs, rules-based extraction, and workflow automation are used responsibly.

Responsibilities

• Design and build automated ingestion pipelines for extracting entity data from company filings, annual reports, regulatory disclosures, third-party documents, and internal sources.
• Develop scalable workflows for document parsing, data extraction, normalization, validation, enrichment, and publishing readiness.
• Implement human-in-the-loop processes that allow data specialists to review, validate, correct, and approve extracted data efficiently.
• Conduct data and document profiling to identify extraction challenges, quality gaps, inconsistencies, and improvement opportunities.
• Implement data lineage, observability, monitoring, and quality measurement frameworks to ensure transparency, traceability, and reliability across ingestion workflows.
• Collaborate with Engineering and Product to define and evolve platform requirements, technical architecture, workflow design, and data quality standards.
• Apply a data product mindset—balancing automation, operational efficiency, data quality, client needs, and long-term maintainability.
• Support integration of AI/LLM-based tools, rules-based logic, and other automation techniques as part of a broader document intelligence and data enrichment strategy.
• Partner with domain experts to design feedback loops that continuously improve extraction accuracy, workflow efficiency, and confidence in automated outputs.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Information Systems, Finance, or a related field, or equivalent professional experience.
• 4+ years of experience in data engineering, data architecture, data automation, or document processing roles.
• Experience working with financial data, especially reference, entity, issuer, or company data domains.
• Strong proficiency in a programming language such as Python, Java, or Scala, and experience with modern data tooling such as Spark, Airflow, Kafka, or equivalent technologies.
• Strong SQL skills for data transformation, validation, quality analysis, and reconciliation.
• Demonstrated experience working with large-scale datasets and complex data pipelines, ideally in reference or entity data domains.
• Experience building automated ingestion or extraction workflows from structured, semi-structured, or unstructured sources.
• Understanding of document processing concepts: parsing, extraction, normalization, validation, metadata capture, and exception handling.
• Experience designing or operating human-in-the-loop workflows for data review, quality control, or operational oversight.
• Deep understanding of data governance, quality frameworks, metadata management, lineage, and auditability.
• Strong analytical mindset and experience with data profiling, validation techniques, and root-cause analysis.
• 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 technical backgrounds.
• Experience applying rules-based logic, AI/ML, or LLM-based tools to automate data extraction, classification, validation, or enrichment workflows.

Nice to have

• Familiarity with financial documents such as company filings, annual reports, prospectuses, regulatory disclosures, or issuer documentation.
• Experience with document AI, OCR, NLP, LLM-based extraction, prompt evaluation, or model-assisted data workflows.
• 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 such as S3, EMR, Glue, ADLS, Data Factory, or Databricks.
• Experience implementing data observability tools such as Monte Carlo, OpenLineage, or custom solutions.
• Experience building feedback loops, annotation workflows, or quality review tooling to improve automated extraction outcomes over time.

Salary & Benefits

Salary Range: 110,000 - 190,000 USD Annual + Benefits + Bonus

Bloomberg offers a comprehensive benefits plan 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) with match, life insurance, and various wellness programs. The Company does not provide benefits directly to contingent workers/contractors and interns.

Apply

If this sounds like you, apply via the Bloomberg careers page.