Senior Data Management Professional - Data Engineering - Entities

Bloomberg•Princeton, FL

About The Position

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. We’re 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. 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. You’ll 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. You’ll 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. You’ll 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.

Requirements

Bachelor’s Degree or Master’s 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.

Nice To Haves

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.

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 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 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.