Senior Data Management Professional - Data Engineering - Entities

Details

Bloomberg’s Data organization delivers data, news, and analytics through innovative technology. The Entities Data Management Team owns the core entity data underpinning Bloomberg’s financial products, including corporate hierarchies, risk attribution, and issuer relationships across public and private markets.

The team is modernizing how data is sourced, extracted, processed, and governed from company filings, annual reports, regulatory disclosures, third-party documents, unstructured content, and internal systems. This includes scalable automated pipelines and human-in-the-loop workflows for document-driven data acquisition, automated extraction, validation, lineage, observability, and quality measurement.

Responsibilities

• Design and build automated ingestion pipelines that extract 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 for data specialists to review, validate, correct, and approve extracted data.
• Conduct data and document profiling to identify extraction challenges, quality gaps, inconsistencies, and process 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 teams to define platform requirements, technical architecture, workflow design, and data quality standards.
• Balance automation, operational efficiency, data quality, client needs, and long-term maintainability using a data product mindset.
• Support the integration of AI/LLM-based tools, rules-based logic, and other automation techniques for document intelligence and data enrichment.
• Partner with domain experts to design feedback loops that 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.
• At least 4 years of experience in data engineering, data architecture, data automation, or document processing.
• Experience working with financial data, particularly reference, entity, issuer, or company data.
• Strong proficiency in Python, Java, Scala, or a similar programming language.
• Experience with modern data tooling such as Spark, Airflow, Kafka, or equivalent technologies.
• Strong SQL skills for data transformation, validation, quality analysis, and reconciliation.
• Experience working with large-scale datasets and complex data pipelines.
• Experience building automated ingestion or extraction workflows from structured, semi-structured, or unstructured sources.
• Understanding of document processing concepts, including 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 skills, including data profiling, validation techniques, and root-cause analysis.
• Ability to work independently and cross-functionally in a fast-evolving environment.
• Excellent communication skills and 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.

Preferred Qualifications

• 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 DCAM or DAMA-DMBOK.
• Experience working with AWS and/or Azure for cloud-native data processing and storage.
• Proficiency with Git and CI/CD pipelines for production-grade deployments.
• Familiarity with 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.

Compensation and Benefits

• Salary range: USD 110,000–190,000 annually, plus benefits and bonus.
• Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) matching, life insurance, and wellness programs.
• Actual compensation may vary based on geographic location, work experience, market conditions, education, training, and skill level.