Reference Data Business Analyst
Job Title: Reference Data Business Analyst
Comp: 170-600k total compensation (candidate dependent)
Ideal YOE: 3-15 years
Summary: A successful hedge fund is looking to bring on a qualified candidate in the reference data operations/business analysis/data analysis space. A qualified candidate will have experience with reference and market data as well as SQL/Python. This candidate will have experience working in a business analysis or product management capacity.
Responsibilities
- Operations experience in financial services, particularly in roles supporting trading and investment activities or associated data
- Responsible for managing daily operations of enterprise reference data systems and associated business processes
- Experience with Reference Data or Security master is preferred
- Required technical skills: SQL, Excel, Python for data analysis and tool building
- Must have experience with equities, futures, options
- Logical thinkers with the ability to communicate with technical and non-technical colleagues alike
- Evaluate current tactical issues, lead small/mid-size initiatives, and contribute to the design and solution of enterprise-wide strategic initiatives
- Design technical solutions data modeling (referential) solutions for new and existing financial products and entities
- Serve as a Subject Matter Expert on the technical workflow and processes involving the reference data solution
- Experience supporting data integration, data governance, data life cycle management, data quality, and data strategy
- Strong product knowledge
- Different types of reference data and data sets
- Client data, account data, security data, pricing data
- Building and supporting reference data systems
Qualifications
- Experience in financial services, asset management, or hedge funds with a focus on data operation and reference data support.
- Expertise in reference data content from common financial service data providers such as Bloomberg, Refinitiv, Barra, FactSet, etc.
- Programming skills in SQL and Python.
- Experience working with large data sets.
- Strong analytic and problem-solving skills, with a keen attention to detail.
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