Vice President/Director, Data Framework Engineering
On-siteTokyo, Tokyo, Japan
Job Summary
Define the strategic roadmap for enterprise data engineering frameworks supporting the bank-wide Data Lakehouse platform. Lead design and lifecycle management of reusable frameworks for ingestion, transformation, serving, orchestration, data quality, reconciliation, lineage, and observability. Establish standards for metadata-driven development, configuration-over-code patterns, and standardized Bronze, Silver, and Gold processing layers. Build scalable serving capabilities for data products, APIs, Delta Sharing, and self-service analytics while driving adoption of Databricks Lakehouse features including Unity Catalog and Spark. Partner with Data Design teams to operationalize canonical models and semantic layers, and lead high-performing regional engineering teams across Data Engineering, Analytics, AI, Risk, Finance, and Regulatory initiatives. Monitor framework KPIs covering onboarding speed, code reuse, deployment frequency, and platform reliability.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Information Systems, Software Engineering, Data Engineering, or related discipline
- 12+ years of experience in enterprise data engineering, data platform engineering, or large-scale data transformation programs
- Proven experience designing and building enterprise-scale data engineering frameworks rather than project-specific pipelines
- Extensive experience developing reusable frameworks that improve delivery speed, developer productivity, platform consistency, and operational resilience
- Demonstrated success implementing metadata-driven and manifest-driven engineering architectures at enterprise scale
- Deep expertise in modern Data Lakehouse platforms, including Databricks, Delta Lake, Spark, Unity Catalog, and cloud-native data services
- Strong hands-on experience building ingestion, transformation, serving, data quality, reconciliation, orchestration, and monitoring frameworks
- Experience designing highly scalable platforms capable of onboarding hundreds of source systems and supporting thousands of data pipelines
- Strong knowledge of distributed processing, Spark optimization, workload management, performance tuning, and large-scale data operations
- Experience implementing CI/CD, DevSecOps, Infrastructure-as-Code, automated testing, observability, and platform engineering best practices
- Hands-on experience with Python, SQL, Spark, PySpark, Git, and modern engineering toolchains
- Strong knowledge of enterprise metadata management, lineage, governance, cataloging, and platforms such as Collibra and Unity Catalog
- Experience enabling self-service analytics, semantic layers, Power BI integration, and data product architectures
Desired Qualifications
- Banking and financial services experience across Risk, Finance, Treasury, Regulatory Reporting, Customer, Compliance, Fraud, and Corporate Banking data domains is highly preferred
- Proven track record delivering enterprise framework platforms that significantly reduce development effort, accelerate onboarding time, improve engineering productivity, and support large-scale regulatory and business data programs
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