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

1. Define and own the strategic roadmap for enterprise data engineering frameworks supporting the bank-wide Data Lakehouse platform.
2. Lead the design, development, and lifecycle management of reusable frameworks for data ingestion, transformation, serving, orchestration, data quality, reconciliation, lineage, and observability.
3. Establish enterprise standards for metadata-driven and manifest-based development, enabling configuration-over-code implementation patterns and accelerated source onboarding.
4. Develop standardized ingestion capabilities supporting batch, streaming, CDC, API, event-driven, file-based, and real-time integration patterns.
5. Build reusable transformation frameworks supporting Bronze, Silver, and Gold data processing layers with configurable business rules and enrichment logic.
6. Define bank-wide framework standards for SCD Type 1, Type 2, Type 3, reference data management, hierarchy processing, and master data integration.
7. Establish enterprise data quality frameworks providing profiling, validation, completeness, accuracy, timeliness, consistency, and integrity controls.
8. Build reconciliations and control frameworks supporting source-to-target balancing, financial controls, regulatory controls, exception management, and operational attestation.
9. Develop scalable data serving frameworks supporting data products, APIs, Delta Sharing, self-service analytics, AI consumption, and Power BI reporting.
10. Lead engineering of framework capabilities for metadata management, lineage, auditability, traceability, security, and policy enforcement.
11. Drive adoption of Databricks Lakehouse capabilities including Delta Lake, Spark, Unity Catalog, Workflows, Auto Loader, and enterprise-scale pipeline standards.
12. Partner with Data Design & Models teams to operationalize canonical models, semantic layers, manifest standards, and reusable engineering patterns.
13. Improve engineering productivity through framework automation, reusable assets, CI/CD pipelines, Infrastructure-as-Code, automated testing, and AI-assisted development.
14. Define and monitor framework KPIs covering onboarding speed, code reuse, deployment frequency, platform reliability, data quality, and developer productivity.
15. Build and lead high-performing regional and offshore engineering teams while providing technology leadership across Data Engineering, Analytics, AI, Risk, Finance, and Regulatory initiatives.

Requirements & Experience

1. Bachelor's or Master's degree in Computer Science, Information Systems, Software Engineering, Data Engineering, or related discipline.
2. 12+ years of experience in enterprise data engineering, data platform engineering, or large-scale data transformation programs.
3. Proven experience designing and building enterprise-scale data engineering frameworks rather than project-specific pipelines.
4. Extensive experience developing reusable frameworks that improve delivery speed, developer productivity, platform consistency, and operational resilience.
5. Demonstrated success implementing metadata-driven and manifest-driven engineering architectures at enterprise scale.
6. Deep expertise in modern Data Lakehouse platforms, including Databricks, Delta Lake, Spark, Unity Catalog, and cloud-native data services.
7. Strong hands-on experience building ingestion, transformation, serving, data quality, reconciliation, orchestration, and monitoring frameworks.
8. Experience designing highly scalable platforms capable of onboarding hundreds of source systems and supporting thousands of data pipelines.
9. Strong knowledge of distributed processing, Spark optimization, workload management, performance tuning, and large-scale data operations.
10. Experience implementing CI/CD, DevSecOps, Infrastructure-as-Code, automated testing, observability, and platform engineering best practices.
11. Hands-on experience with Python, SQL, Spark, PySpark, Git, and modern engineering toolchains.
12. Strong knowledge of enterprise metadata management, lineage, governance, cataloging, and platforms such as Collibra and Unity Catalog.
13. Experience enabling self-service analytics, semantic layers, Power BI integration, and data product architectures.
14. Banking and financial services experience across Risk, Finance, Treasury, Regulatory Reporting, Customer, Compliance, Fraud, and Corporate Banking data domains is highly preferred.
15. 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.