Reference Data Business Analyst

QRT is a global quantitative and systematic investment manager, operating across all liquid asset classes. We are a data- and technology-driven organization applying scientific methods to investing. Our collaborative culture and focus on innovation enable us to solve complex challenges and deliver high-quality returns for our investors. The newly formed Reference Data Technology Team is building a new reference data system.

The team works with stakeholders across Trading, Operations, Compliance and Research to translate business and regulatory requirements into data models, datasets and data quality controls. Your future role within QRT We are seeking an exceptional Reference Data Business Analyst to join our Reference Data Technology Team. The role is expected to involve the following activities:

• Translate business and regulatory requirements into data entity models and datasets for the new reference data system.
• Work closely with the Data/Platform Architect to ensure business requirements map cleanly onto the technical model.
• Gather and validate requirements directly with domain stakeholders across Trading, Compliance and Research.
• Partner with Trading, Operations and Compliance teams to build automated data quality rules and checks, exception-management workflows and metrics.
• Provide day-to-day support to business, compliance and technology teams as a subject matter expert on data queries and system behaviour.
• Apply expert knowledge of equity reference data, related events, and the lifecycle and processing of corporate actions.
• Support the initial delivery focused on Equities and ETFs and its direct impact on the trading business.
• Produce data dictionaries, entity relationship diagrams and requirements specifications. Your present skillset
• Experience producing entity and data models for reference or master data platforms in financial services.
• Deep working knowledge of ETF reference data, including composition and basket data, creation and redemption mechanics, and identifiers.
• Hands-on knowledge of ETF corporate actions processing, including dividends, splits, mergers and delistings.
• Understanding of corporate actions lifecycle stages, timing and effective-date handling, and the downstream impact of processing errors.
• Experience defining corporate actions data quality rules, including the detection of missed or conflicting action records and effective-date validation.
• Strong requirements-gathering skills across multiple stakeholder groups, including Trading, Compliance and Operations.
• Working knowledge of data quality concepts, including completeness, accuracy, timeliness and referential integrity.
• Ability to translate regulatory and business rules into structured, testable data rules.
• Comfortable working directly with technical architects to map business requirements onto a reference data model.
• Strong written documentation skills, including data dictionaries, entity relationship diagrams and requirements specifications.
• Strong database and SQL skills for data interrogation and analysis. Desirable to have experience or a willingness to learn in the following areas
• Client and account reference data, including legal entity structures, KYC, account hierarchies and LEIs.
• Master data management concepts, including golden source, survivorship and entity resolution.
• Scoping an MVP and maintaining a firm line on deferred scope.
• Data lineage, metadata cataloguing and associated tooling.
• Data modelling tools, including entity relationship diagramming and data catalogue tools.
• UK and EU financial services regulatory frameworks relevant to reference data, including the LEI regime and KYC/CDD requirements.
• Python experience and knowledge. QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

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