VP/Director, Data Analysis Lead - SMBC
Core
Lead enterprise-wide data analysis activities, establishing standards and governance for onboarding diverse data domains into a Data Lakehouse platform.
Role type
VP/Director, Enterprise Data Analysis Lead
Builds
Enterprise data platform (Data Lakehouse) supporting business, risk, finance, compliance, treasury, and regulatory domains
Domain
Banking / Enterprise Data Management
Deliverable
production ML models
Required skills
Data analysis, Source-to-Target Mapping (STM), Data lineage analysis, SQL, Python, Data profiling, Data quality controls, Regulatory reporting (BCBS239), Data governance, Stakeholder management, Team leadership
Preferred skills
Databricks, Delta Lake, Spark, Collibra, Data cataloging, Metadata-driven analysis
Technologies
Databricks, Delta Lake, Spark, Collibra, SQL, Python
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.
Seniority
VP/Director, Strategy & Mentorship