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Data Analyst — Regulatory Reporting

Summary

Data Analyst builds and validates data pipelines for MAS 610 regulatory reports, mapping finance sources to reporting models and reconciling outputs against the General Ledger.

Key Responsibilities

  • Gather, analyse, and document data requirements for regulatory reporting, with a particular focus on MAS 610 and related regulatory submissions.
  • Define and maintain source-to-target data mappings between upstream banking systems, finance data sources, regulatory reporting data models, and reporting templates.
  • Work alongside the IT-led regulatory reporting platform implementation to ensure data requirements are complete, accurate, traceable, and aligned with reporting logic.
  • Analyse regulatory reporting data dictionaries, data models, taxonomy, and reporting attributes and translate regulatory requirements into detailed data specifications.
  • Perform hands-on data validation and reconciliation to confirm the completeness and accuracy of data flowing into the regulatory reporting solution.
  • Define data quality rules, validation controls, exception criteria, and testing scenarios for regulatory reporting data.
  • Execute data quality testing, investigate exceptions, identify root causes, and coordinate remediation with relevant business and technology teams.
  • Support the mapping and documentation of Finance and Regulatory Reporting processes, including manual adjustments, journals, reconciliations, and reporting controls.
  • Perform reconciliations of regulatory reporting outputs and underlying data back to the General Ledger and other Finance systems.
  • Identify gaps between current-state data and target regulatory reporting requirements and recommend appropriate remediation or transformation rules.
  • Support system integration testing, user acceptance testing, parallel runs, and implementation activities related to regulatory reporting data.
  • Work closely with Regulatory Reporting, Finance, Data, Risk, and Technology stakeholders to resolve data issues and ensure reporting requirements are understood consistently.
  • Produce clear documentation covering data lineage, mapping logic, validation rules, reconciliations, data quality controls, and issue resolution.
  • Where appropriate, develop dashboards or visualizations to communicate data quality, reconciliation results, exceptions, and reporting trends.

Required Experience

  • 5+ years of experience working with regulatory reporting data within banking or financial services, preferably in an APAC regulatory environment.
  • Demonstrable hands-on experience with MAS 610 regulatory reporting, including data mapping, validation, reconciliation, and reporting requirements.
  • Strong experience translating regulatory reporting requirements into detailed data requirements and source-to-target mappings.
  • Experience working alongside or supporting a regulatory reporting platform such as:
  • Regnology

    Agile Reporter

    Axiom / AxiomSL

  • Strong understanding of regulatory reporting data dictionaries, data lineage, reporting attributes, and data models.
  • Hands-on experience defining and testing data quality rules and controls.
  • Strong SQL skills, with the ability to query, analyse, reconcile, and validate large and complex datasets.
  • Experience with Finance processes relevant to regulatory reporting, including:
  • General Ledger reconciliation

    Manual adjustments and journals

  • Data aggregation and transformation
  • Reporting controls and exception management
  • Experience supporting regulatory reporting testing activities, including SIT, UAT, parallel runs, and data validation.

Preferred Skills

  • Experience with Power BI or similar data visualization tools for building data quality, reconciliation, or management reporting dashboards.
  • Broader knowledge of Singapore and APAC banking regulatory reporting requirements.
  • Experience working on regulatory reporting transformation or platform implementation Programmes.
  • Understanding of banking products, Finance data architecture, and upstream source systems.
  • Familiarity with data governance, metadata management, data lineage, and data quality frameworks.

See also

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