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.

VP/Director, Data Analysis Lead

Director/VP of Engineering