Senior Data Management Professional - Data Engineering - Corporate Actions
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
API @ 4
Agile @ 3
Communication @ 6
Data Engineering @ 6
Data Modeling @ 7
Data Pipelines @ 6
Data Science
ELT @ 6
ETL @ 6
LLM
LangChain @ 4
Machine Learning @ 4
NLP @ 4
NoSQL @ 4
Observability
Pandas @ 6
Python @ 6
SQL @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Bloomberg’s products are fueled by powerful information. The Data organization delivers data, news, and analytics through innovative technology, applying problem-solving skills to identify workflow efficiencies and implement technology solutions that enhance systems, products, and processes.
The team manages equity corporate actions data, including dividends, stock splits, and rights offerings, as well as equities reference data. This data supports internal and external partners such as Enterprise Data, Indices, and News, and serves as a foundational component of workflows used by financial market professionals across North American and global capital markets.
The role focuses on the technical evolution of Equity Corporate Actions data products through hands-on data engineering and the practical application of AI and large language models for automated data extraction, validation, and transformation into an enterprise-grade data model.
Responsibilities
- Design, build, and optimize scalable data pipelines using Python, SQL, workflow orchestration, distributed processing, messaging frameworks, and cloud-based data platforms.
- Develop data architectures and automated ingestion frameworks for structured and unstructured data sources.
- Design scalable, high-performance data processing solutions, schema structures, and interoperability across downstream systems.
- Apply AI, large language models, natural language processing, and machine learning to extract, normalize, and enrich corporate actions data from issuer filings, regulatory disclosures, news, press releases, exchange feeds, and other complex sources.
- Implement Human-in-the-Loop workflows and data quality frameworks combining AI-driven extraction with automated validation, business rules, statistical methods, and exception management.
- Create data quality dashboards, pipeline health metrics, monitoring, reporting, observability, and SLA monitoring capabilities.
- Partner with Product, Engineering, Data Science, and business stakeholders to design scalable data solutions, standardize engineering practices, and deliver data products supporting trading, analytics, and client-facing applications.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Quantitative Finance, or an equivalent quantitative discipline.
- At least 3 years of hands-on experience in data engineering or technical data management, including building scalable production ETL/ELT pipelines.
- Advanced Python proficiency, including Pandas, PySpark, or standard data manipulation libraries.
- Experience with complex SQL and NoSQL database engineering.
- Experience applying AI, large language models, and machine learning for structured and unstructured document processing and automated information extraction, including technologies such as LangChain, LlamaIndex, AI-assisted APIs, Hugging Face, or custom NLP models.
- Experience with modern data technology stacks, including workflow orchestration engines, message streaming platforms, distributed computing frameworks, and object storage systems.
- Strong data modeling and schema design skills for analytical capabilities.
- Exceptional problem-solving abilities, numerical proficiency, attention to detail, and communication skills.
Preferred Qualifications
- Experience ingesting, normalizing, and processing exchange-disseminated United States equity corporate actions data, including dividends, stock splits, and rights offerings, and equity reference data.
- Industry certifications such as Certified Data Management Professional or Data Capability Assessment Model.
- Experience designing Human-in-the-Loop operational tooling and exception management workflows.
- Familiarity with Agile methodologies, backlog management, and modern data governance frameworks.
Compensation And Benefits
Salary range: USD 110,000–190,000 annually, plus benefits and bonus. Actual compensation may vary based on geographic location, work experience, market conditions, education, training, and skill level.
Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, a 401(k) match, life insurance, and wellness programs.