Senior Data Management Professional Data Engineering Entities

Bloomberg

Job Location:

Princeton, NJ - USA

Monthly Salary: Not Disclosed

Posted on: 13 hours ago

Vacancies: 1 Vacancy

Job Summary

Senior Data Management Professional - Data Engineering - Entities

Location

Princeton

Business Area

Data

Ref #

Description & Requirements

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 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.

Our Team:

The Entities Data Management Team owns the core entity data that underpins Bloombergs financial products including corporate hierarchies risk attribution and issuer relationships across public and private markets. Were modernizing how this data is sourced extracted processed and governedespecially from company filings annual reports regulatory disclosures third-party documents unstructured content and internal systems.

We are building scalable automated pipelines and human-in-the-loop workflows to ingest and transform data from high-value documents with the accuracy transparency and governance that Bloomberg clients expect. As part of this effort we are implementing new architecture for document-driven data acquisition automated extraction validation lineage observability and quality measurement.

The Role:

We are looking for a Senior Data Automation Engineer who operates at the intersection of data engineering document intelligence and data product strategy. Youll help 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.

This role requires someone with a strong understanding of entity and reference data as well as the technical acumen to design and operate scalable data pipelines for complex document-based workflows. Youll be expected to profile source documents and extracted datasets evaluate quality and consistency and improve automation workflows with a strong focus on data lineage observability governance and human-in-the-loop oversight.

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. Youll play a key role in shaping how AI LLMs rules-based extraction and workflow automation are used responsibly to accelerate ingestion while maintaining the quality and auditability expected of Bloomberg data.

Well trust you to:

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 opportunities for process improvement.

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 mindsetbalancing automation operational efficiency data quality client needs and long-term maintainability.

Support the 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.

Youll need to have:

Bachelors Degree or Masters Degree in Computer Science Mathematics Information Systems Finance or a related field or equivalent professional work experience

4 years of experience in data engineering data architecture data automation or document processing roles.

Experience working with financial data especially within 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 domains such as reference or entity data.

Experience building automated ingestion or extraction workflows from structured semi-structured or unstructured sources.

Understanding of document processing concepts such as 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 levels of technical knowledge.

Experience applying rules-based logic AI/ML or LLM-based tools to automate data extraction classification validation or enrichment workflows.

Wed love to see:

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.

If this sounds like you:

Apply! If you think were a good match. Well get in touch to let you know the next steps!

Salary Range 00 USD Annual Benefits Bonus

The referenced salary range is based on the Companys good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location work experience market conditions education/training and skill level.

We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards 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) match life insurance and various wellness programs among others. The Company does not provide benefits directly to contingent workers/contractors and interns.

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Required Experience:

Senior IC

Senior Data Management Professional - Data Engineering - Entities Location ...