VP, Data Engineer

SMBC Group is a top-tier global financial group. Headquartered in Tokyo and with a 400-year history, SMBC Group offers a diverse range of financial services, including banking, leasing, securities, credit cards, and consumer finance. The Group has more than 130 offices and 80,000 employees worldwide in nearly 40 countries. Sumitomo Mitsui Financial Group, Inc. (SMFG) is the holding company of SMBC Group, which is one of the three largest banking groups in Japan. SMFG’s shares trade on the Tokyo, Nagoya, and New York (NYSE: SMFG) stock exchanges. In the Americas, SMBC Group has a presence in the US, Canada, Mexico, Brazil, Chile, Colombia, and Peru. Backed by the capital strength of SMBC Group and the value of its relationships in Asia, the Group offers a range of commercial and investment banking services to its corporate, institutional, and municipal clients. It connects a diverse client base to local markets and the organization’s extensive global network. The Group’s operating companies in the Americas include Sumitomo Mitsui Banking Corp. (SMBC), SMBC Nikko Securities America, Inc., SMBC Capital Markets, Inc., SMBC MANUBANK, JRI America, Inc., SMBC Leasing and Finance, Inc., Banco Sumitomo Mitsui Brasileiro S.A., and Sumitomo Mitsui Finance and Leasing Co., Ltd. Role Description We are seeking an experienced Vice President – Data Engineer with 10–15 years of hands‑on experience to lead the design, development, and optimization of scalable, cloud‑native data platforms. This role requires deep technical expertise in Azure, Databricks, PySpark, Python, and SQL, supporting enterprise data pipelines for regulatory, compliance, and analytics workloads. The ideal candidate will operate at both strategic and hands‑on levels, delivering reliable, secure, and high‑performance data solutions in a highly regulated investment banking environment. Key Responsibilities Own the architecture, design, and implementation of end‑to‑end ETL/ELT workflows using Azure Data Factory (ADF) and Azure Databricks for regulatory and compliance‑driven data ingestion and transformation. Integrate, standardize, and normalize structured and unstructured data from multiple internal and external sources while enforcing strict data quality and governance controls. Build secure, auditable, and high‑performance data pipelines supporting large‑scale, sensitive financial datasets. Automate ingestion, transformation, and validation processes to enable near real‑time analytics and regulatory reporting. Design and implement efficient storage formats, partitioning, and optimization strategies for fast data access and retrieval. Utilize Databricks and Delta Lake for distributed processing, ACID‑compliant storage, versioning, and time‑travel capabilities. Enforce data retention, archiving, and purging policies aligned with global regulatory and compliance requirements. Drive the migration of legacy application logic into modern Azure Databricks, Data Lake, and SQL‑based architectures. Maintain comprehensive data lineage, metadata management, and audit trails using Azure Purview or equivalent frameworks. Partner with data governance, risk, and compliance teams to define data standards, access controls, and security requirements. Implement and manage CI/CD pipelines using GitHub and GitHub Actions, enabling automated testing, version control, and reliable deployments. Review code, enforce engineering best practices, and support production deployments and operational stability. Participate in Agile/Scrum ceremonies, including sprint planning, design reviews, and regulatory or audit engagements. Provide technical leadership and mentorship to data engineers, setting standards and best practices across the organization. Qualifications Master’s degree in Computer Science, Engineering, Information Systems, or a related field. 10–15 years of hands‑on experience in data engineering, preferably within financial services or other regulated industries. Required Technical Expertise Azure Databricks (clusters, jobs, notebooks, Delta Lake, performance tuning) PySpark (RDDs, DataFrames, Spark SQL, optimization techniques) Azure Cloud Services: Azure Data Factory (ADF) ADLS Gen2 Azure Synapse Azure Functions Azure DevOps / GitHub Python for data engineering and automation workflows SQL (complex queries, performance optimization, large‑scale datasets) SMBC’s employees participate in a Hybrid workforce model that provides employees with an opportunity to work from home, as well as, from an SMBC office. SMBC requires that employees live within a reasonable commuting distance of their office location. Prospective candidates will learn more about their specific hybrid work schedule during their interview process. Hybrid work may not be permitted for certain roles, including, for example, certain FINRA‑registered roles for which in‑office attendance for the entire workweek is required. SMBC provides reasonable accommodations during candidacy for applicants with disabilities consistent with applicable federal, state, and local law. If you need a reasonable accommodation during the application process, please let us know at View email address on click.appcast.io. #J-18808-Ljbffr SMBC Group