Job Description

What You’ll Do


  • 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

    • What You Bring

  • 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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