Senior Data Management Professional - Data Engineering - Corporate Actions

Bloomberg•Princeton, NJ

1d•$110,000 - $190,000•Onsite

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 is responsible for the end-to-end data management of equity corporate actions data (including dividends, stock splits, and rights offerings) as well as equities reference data to offer a comprehensive product offering for our internal and external partners such as Enterprise Data, Indices and News. Multi-functional collaboration, deep domain knowledge, thoughtful automation, and data management expertise are paramount for our ability to continuously deliver high quality data to our rapidly growing client base. Equity Corporate Actions and Reference data serve as foundational building blocks across our overall offering, supporting critical workflows for hundreds of thousands of financial market professionals across North America and global capital markets. We are seeking a highly motivated, hands-on Senior Data Management Professional (DMP) - Data Engineering based in Princeton, NJ, to drive the technical evolution of our Equity Corporate Actions data products. In this role, you will act as a technical leader, navigating ambiguity to solve complex data challenges and engineer scalable, production-ready solutions. This role is heavily focused on hands-on data engineering and the practical application of AI/LLMs for automated data extraction, validation and transformation to enterprise grade data model. You will design, build, and maintain high-throughput ETL pipelines, architect robust data models, and deploy intelligent automation frameworks to ingest and parse structured and unstructured financial data at scale.

Requirements

Bachelor’s Degree or Master’s Degree in Computer Science, Data Engineering, Information Systems, Quantitative Finance, or an equivalent quantitative discipline.
3+ years of hands-on experience in a Data Engineering or technical Data Management role, with a proven track record of building scalable ETL/ELT pipelines in a production environment.
Advanced technical proficiency in Python (Pandas, PySpark, or standard data manipulation libraries) and complex SQL/NoSQL database engineering.
Hands-on experience applying AI/LLMs and Machine Learning (e.g., LangChain, LlamaIndex, AI Assisted APIs, Hugging Face, or custom NLP models) for structured/unstructured document processing and automated information extraction.
Demonstrated experience with modern data tech stacks, including workflow orchestration engines, message streaming platforms, distributed computing frameworks, and object storage systems.
Strong data modeling and schema design skills, with experience creating structures optimized for analytical capabilities.
Exceptional problem-solving abilities, numerical proficiency, high attention to detail, and strong communication skills to present technical concepts to diverse stakeholders.

Nice To Haves

Direct experience ingesting, normalizing, and processing exchange-disseminated US Equity Corporate Actions data (e.g., dividends, stock splits, rights offerings) and equity reference data.
Industry certifications such as Certified Data Management Professional (CDMP) or Data Capability Assessment Model (DCAM).
Experience designing Human-in-the-Loop operational tooling and exception management workflows.
Familiarity with Agile methodologies, backlog management, and modern data governance frameworks.

Responsibilities

Design, build, and optimize scalable data pipelines to ingest, transform, and deliver high-volume financial data using Python, SQL, and modern enterprise data technologies, including workflow orchestration, distributed processing, messaging frameworks, and cloud-based data platforms.
Develop robust data architectures and automated ingestion frameworks that support structured and unstructured data sources, enabling scalable, high-performance data processing, schema design, and seamless interoperability across downstream systems.
Leverage AI, Large Language Models (LLMs), NLP, and machine learning to extract, normalize, and enrich Corporate Actions data (e.g., dividends, stock splits, rights offerings) from issuer filings, regulatory disclosures, news, press releases, exchange feeds, and other complex data sources.
Implement intelligent Human-in-the-Loop (HITL) workflows and data quality frameworks that combine AI-driven extraction with automated validation, business rules, statistical methods, and exception management to maximize accuracy, completeness, and operational efficiency.
Develop monitoring, reporting, and observability solutions by creating data quality dashboards, pipeline health metrics, and SLA monitoring capabilities that provide visibility into data integrity, processing performance, and operational effectiveness.
Partner cross-functionally with Product, Engineering, Data Science, and business stakeholders to design scalable data solutions, standardize engineering best practices, and deliver high-quality data products that support trading, analytics, and client-facing applications.