Role DescriptionWe 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.
