VP/Director, Data Analysis Lead
On-siteTokyo, Tokyo, Japan
Job Summary
Lead enterprise-wide data analysis activities supporting onboarding of business, risk, finance, compliance, treasury, customer, and regulatory data domains into the Data Lakehouse platform. Drive source system discovery, source-of-record identification, and golden source determination while establishing data analysis standards, methodologies, templates, and governance processes. Define and maintain enterprise standards for Source-to-Target Mapping (STM), data mapping specifications, transformation logic documentation, and reconciliation requirements. Partner with business stakeholders, data owners, architects, and engineering teams to translate business requirements into engineering-ready data requirements. Own data sourcing strategies for structured, semi-structured, and unstructured data, analyze end-to-end data lineage, and define business rules, derivation logic, and data quality controls. Lead data profiling, discovery, and assessment to identify quality issues, gaps, and remediation opportunities. Develop canonical data mappings and semantic definitions to improve consistency, drive adoption of metadata-driven analysis using Collibra, and lead teams of data analysts. Support BCBS239, regulatory reporting, risk aggregation, data governance, and audit requirements through comprehensive traceability.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Information Systems, Data Management, Engineering, Finance, or related discipline
- 12+ years of experience in Data Analysis, Data Engineering, Data Management, Regulatory Reporting, or Enterprise Data programs
- Proven experience leading large-scale data sourcing and analysis initiatives within enterprise data platforms or Lakehouse environments
- Extensive experience identifying systems of record, golden sources, authoritative datasets, and enterprise data ownership structures
- Deep expertise in Source-to-Target Mapping (STM), field-level mapping specifications, transformation logic documentation, and data lineage analysis
- Strong hands-on experience using SQL for data profiling, data discovery, data quality analysis, reconciliation, and metadata analysis
- Practical programming experience using Python for data exploration, profiling, reconciliation analysis, and automated data validation
- Experience analysing large and complex source systems across operational, transactional, regulatory, and analytical environments
- Strong understanding of relational, dimensional, normalized, denormalized, and Lakehouse-based data structures
- Experience working with Databricks, Delta Lake, Spark, Collibra, data catalogs, metadata repositories, and modern data platform technologies
- Strong understanding of data integration patterns including APIs, CDC, streaming, files, messaging systems, and event-driven architectures
- Experience defining business rules, data quality controls, reconciliation requirements, and data validation standards for enterprise platforms
- Strong banking domain knowledge across Credit Risk, Market Risk, Finance, Regulatory Reporting, Treasury, Customer, Transaction Banking, Compliance, and Financial Crime domains
- Deep understanding of BCBS239 principles, regulatory data controls, governance requirements, and audit traceability expectations
- Proven experience establishing enterprise-scale data analysis frameworks, standards, and practices that improve onboarding speed, reduce ambiguity, increase engineering productivity, and enable consistent delivery across multiple programs and regions
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