Senior Data Management Professional - Data Engineering - Corporate Actions

AI in this role

langchainllamaindexhugging-face

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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:
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.
The Role:
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.
We’ll trust you to:

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.

You’ll need to have:

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.

We’d love to see:

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.

If this sounds like you:Apply! If you think we're a good match. We'll get in touch to let you know the next steps!

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 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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How we rate this

Senior Data Management Professional - Data Engineering - Corporate Actions at Bloomberg rates 47 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.

Classification

Uses AI. An ordinary role that requires AI tools.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI AutomationNLPLangChainLlamaIndexHugging Face

Questions you could be asked

Tell me about a workflow you automated with AI tools, end to end.
What NLP problem have you worked on, and how did you measure whether it actually worked?
What are the limits of LangChain that you've run into, and how did you work around them?
What's a project where you used LlamaIndex hands-on?
Walk me through how you've used Hugging Face in your day-to-day work.

Adapt your resume

List these exact terms on your resume: AI Automation, NLP, LangChain, LlamaIndex, and Hugging Face. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Put the AI tool in a bullet point about what you did, not just in a skills list — this role treats it as a required part of the job.

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