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VP/Director, Data Analysis Lead

SMBC Group
12-14 Years
Singapore
Early Applicant
  • Posted 8 days ago
  • Be among the first 10 applicants

Job Description

Key Responsibilities

  • 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 for strategic data initiatives.
  • Establish data analysis standards, methodologies, templates, and governance processes across the data engineering organization.
  • Define and maintain enterprise standards for Source-to-Target Mapping (STM), data mapping specifications, transformation logic documentation, and reconciliation requirements.
  • Lead detailed analysis of source system data models, schemas, interfaces, APIs, data feeds, messaging formats, and integration patterns.
  • Partner with business stakeholders, data owners, architects, and engineering teams to translate business requirements into engineering-ready data requirements.
  • Own data sourcing strategies for onboarding structured, semi-structured, and unstructured data into the enterprise data platform.
  • Analyse and document end-to-end data lineage across source systems, operational platforms, warehouses, regulatory platforms, and analytical environments.
  • Define business rules, derivation logic, data quality requirements, controls, reconciliation requirements, and operational data standards.
  • Lead data profiling, data discovery, and data assessment activities to identify data quality issues, gaps, anomalies, and remediation opportunities.
  • Develop canonical data mappings and enterprise semantic definitions to improve consistency and reuse across domains.
  • Partner with Data Design and Data Framework Engineering teams to ensure requirements can be implemented using enterprise standards and reusable frameworks.
  • Support BCBS239, regulatory reporting, risk aggregation, data governance, and audit requirements through comprehensive source-to-consumption traceability.
  • Drive adoption of metadata-driven analysis, business glossary standards, and governance practices leveraging Collibra and related platforms.
  • Lead and mentor teams of data analysts while establishing analysis quality metrics, delivery standards, and best practices across regional and global teams.

Requirements & Experience

  • 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.

More Info

Job Type:
Permanent Job
Industry:
Other
Function:
Data Analysis
Employment Type:
Full time

About Company

Job ID: 152389381

Last Updated: 16-08-2026 06:15:26 PM
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