- Singapore
Kerja Sepenuh Masa, VP-Director, Data Analysis Lead di SMBC Group - Maukerja
Kongsi
Simpan
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
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.
- 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.
Peringatan Penting
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