VP/Director, Data Analysis Lead

The Japan Research Institute, Limited (Singapore Branch); Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Sumitomo Mitsui Banking Corporation (SMBC) is seeking an experienced data leader to drive enterprise data analysis, sourcing, and governance across banking domains.

The role focuses on STM, data lineage, and regulatory reporting in a Lakehouse environment, leveraging Databricks, Collibra, and modern data platform technologies. Strong leadership and BCBS239 expertise are essential.

Qualifications

• Bachelor's or Master's degree in a relevant field.
• 12+ years of experience in data analysis and related domains.
• Proven leadership of large-scale data sourcing initiatives.
• Expertise in STM, data lineage, and governance.
• Strong banking domain knowledge and BCBS239 exposure.

Responsibilities

• Lead enterprise-wide data analysis activities for onboarding data into the Data Lakehouse platform.
• Drive source system discovery and golden source determination for strategic data initiatives.
• Establish enterprise data analysis standards, templates, and governance processes.
• Define STM, mapping specifications, transformation logic documentation, and reconciliation requirements.
• Lead analysis of source system data models, interfaces, and data feeds.
• Collaborate with stakeholders to translate business requirements into data requirements.
• Own data sourcing strategies for structured, semi-structured, and unstructured data.
• Document end-to-end data lineage across source systems and platforms.
• Define business rules, data quality controls, and validation standards.
• Lead data profiling and discovery to identify data quality issues and remediation opportunities.
• Develop canonical data mappings and enterprise semantic definitions.
• Work with Data Design and Framework teams to implement enterprise standards.
• Support BCBS239, regulatory reporting, risk aggregation, and audit traceability.
• Promote metadata-driven analysis and governance leveraging Collibra and related tools.
• Lead and mentor data analysts across regional and global teams.

Skills

SQL

Python

Data analysis

Data engineering

Data governance

Stakeholder management

Team leadership

BCBS239

Regulatory reporting

Data lineage

Delta Lake

Spark

Databricks

APIs

Education

Bachelor's or Master's degree in CS/IS/Data Management/Engineering/Finance

Tools

Databricks

Delta Lake

Spark

Collibra

Data catalogs

Metadata repositories

Job description

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Headquartered in Tokyo, Sumitomo Mitsui Banking Corporation (SMBC) is a leading global financial institution and a core member of Sumitomo Mitsui Financial Group (SMBC Group). Built upon our rich Japanese heritage since 1876, we put our customers first and provide seamless access to, from and within the Asia Pacific region. SMBC is one of the largest Japanese banks by assets and maintain strong credit ratings across our global integrated network. We work closely as one SMBC Group to offer personal, corporate and investment banking services to meet the needs of our customers.

With sustainability embedded within our strategy and operations, we are committed to creating a society in which today’s generation can enjoy economic prosperity and well-being, and pass it on to future generations.

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.