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.

Our 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. The team is building scalable, automated pipelines and human-in-the-loop workflows to ingest and transform data from high-value documents with accuracy, transparency, and governance.

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. You will 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 a strong understanding of entity and reference data and technical acumen to design and operate scalable data pipelines for complex document-based workflows.

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 enable data specialists to review, validate, correct, and approve extracted data.
• 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 mindset — balancing automation, operational efficiency, data quality, client needs, and maintainability.
• Support integration of AI/LLM-based tools, rules-based extraction, and workflow automation while maintaining quality and auditability.
• Partner with domain experts to design feedback loops that continuously improve extraction accuracy and workflow efficiency.

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 with financial data, especially reference, entity, issuer, or company data domains.
• Strong proficiency in a programming language such as Python, Java, or Scala.
• 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 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: parsing, extraction, normalization, validation, metadata capture, and exception handling.
• Experience designing or operating human-in-the-loop workflows for data review and quality control.
• Deep understanding of data governance, quality frameworks, metadata management, lineage, and auditability.
• Strong analytical mindset with experience in data profiling, validation techniques, and root-cause analysis.
• Excellent communication skills and ability to explain technical decisions to varied stakeholders.
• Experience applying rules-based logic, AI/ML, or LLM-based tools to automate extraction, classification, validation, or enrichment workflows.

Preferred / Nice to Have

• Familiarity with financial documents (company filings, annual reports, prospectuses, regulatory disclosures).
• Experience with document AI, OCR, NLP, LLM-based extraction, prompt evaluation, or model-assisted workflows.
• Familiarity with data governance frameworks such as 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 production deployments.
• Familiarity with cloud data services: 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.

Compensation & Benefits

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

The referenced salary range is based on the company's good faith belief at the time of posting. Actual compensation may vary based on geographic location, experience, market conditions, education/training, and skill level.

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, 401(k) with match, life insurance, and wellness programs. Benefits are not provided directly to contingent workers/contractors and interns.

How to Apply

Apply via the Bloomberg careers site linked in the original posting.