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Reference Data BA

Madison-Davis

  • Charlotte, NC
  • 30+ days ago
  • $64

Highlights

Work directly with large datasets using SQL to validate data quality, perform reconciliations, and support root-cause analysis. Analyze and document reference data models including securities, instruments, issuers, identifiers, hierarchies, and relationships.

Numbers & Facts

LocationCharlotte, NC
Salary$64

Description


RESPONSIBILITIES
  • Serve as a lead Business Analyst for the build-out of an enterprise Security Master covering multiple asset classes.
  • Analyze and document reference data models including securities, instruments, issuers, identifiers, hierarchies, and relationships.
  • Work directly with large datasets using SQL to validate data quality, perform reconciliations, and support root-cause analysis.
  • Partner with data engineering teams to translate business requirements into logical and physical data designs.
  • Define data lineage, ownership, and usage for market data, reference data, and capital markets transaction data.
  • Support integration of external data sources such as ratings, indices, pricing feeds, and vendor reference data.
  • Collaborate with AI and analytics teams on enrichment, scoring, and entity-linking use cases.
  • Drive clarity across ambiguous data problems by aligning stakeholders on definitions, rules, and governance.
  • Produce high-quality documentation including business requirements, data mappings, and functional specifications.

QUALIFICATIONS
  • 7+ years of experience as a Business Analyst or Data Analyst within financial services.
  • Deep, hands-on expertise with reference data and security master concepts.
  • Strong working knowledge of capital markets data, including instruments, trades, positions, and lifecycle events.
  • Advanced SQL skills with experience querying large, complex datasets.
  • Experience working with market data vendors, identifiers, and symbology (e.G., securities, issuers, hierarchies).
  • Proven ability to partner closely with engineering, data, and product teams.
  • Strong analytical mindset with the ability to connect disparate datasets into a coherent model.
  • Excellent communication skills with the ability to translate complex data topics to non-technical stakeholders.
  • Experience supporting enterprise data platforms or large-scale data modernization initiatives.
  • Exposure to data governance, metadata management, or data quality frameworks.
  • Familiarity with ratings, indices, or alternative data sources.
  • Experience supporting AI or machine learning initiatives from a data definition perspective.
  • Background working in front office, risk, or operations data environments.

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