Role DescriptionSMBC is driving a major Digital Transformation initiative across its Americas Division, with a strong focus on modernizing technology platforms, enhancing data-driven decision-making, and supporting business growth. As part of this transformation, we are seeking a talented
Data Engineer to join the
Regulatory Reporting & CCAR Technology Team.
The Data Engineer will be responsible for developing scalable data platforms on Databricks that support end-to-end data sourcing, CCAR model execution, data adjustments, result overlays, and attestation workflows. This role requires strong expertise in Databricks, PySpark, Python, and AI/ML technologies to build robust, auditable, and regulatory-compliant solutions. The individual will collaborate closely with Finance, Risk, Data Management, and Model Development teams to deliver innovative data engineering solutions that enhance regulatory reporting processes and operational efficiency.
Key Responsibilities- Design, develop, and support scalable data pipelines using Databricks, PySpark, Python, SQL, and Delta Lake.
- Build and maintain data sourcing and ingestion frameworks for CCAR, Stress Testing, and Regulatory Reporting.
- Develop and support platforms for CCAR model execution, including PPNR, Balance Sheet, RWA, and other risk models.
- Implement data quality controls, reconciliation processes, lineage tracking, and audit capabilities.
- Develop solutions for data adjustments, management overlays, business overrides, and exception management workflows.
- Build and enhance attestation, approval, and sign-off workflows for regulatory reporting governance.
- Partner with Finance, Risk, and Regulatory Reporting stakeholders to deliver business-critical solutions.
- Leverage AI/ML capabilities for data quality monitoring, anomaly detection, intelligent automation, and operational insights.
- Support CI/CD, production releases, troubleshooting, performance tuning, and operational support activities.
Qualifications and Skills- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
- 7+ years of experience in Data Engineering, Data Platform Development, or Analytics Engineering.
- Strong hands-on experience with: Azure Data Factory, Databricks, PySpark, Python, SQL, Delta Lake, GitHub and CI/CD
- Experience building cloud-native and distributed data processing solutions.
- Strong understanding of ETL/ELT architectures, data modeling, data governance, and data quality frameworks.
- Knowledge of AI/ML concepts, machine learning lifecycle management, and model operationalization.
- Excellent analytical, problem-solving, communication, and stakeholder management skills.
Preferred Qualifications- Experience supporting CCAR, FR Y-14 Regulatory Reporting, Stress Testing, Capital Planning, Risk Management, or Finance Technology initiatives.
- Experience developing solutions for:
- Data sourcing and reconciliation
- Model execution platforms
- Data adjustments and result overlays
- Attestation and workflow management
- Experience with MLflow, Databricks Workflows, Azure Data Factory, Azure SQL, Azure Functions, REST APIs, and Azure Cloud Services.
- Experience migrating analytical models from R to Python and deploying AI/ML solutions into production.
- Knowledge of model governance, model risk management, audit controls, and regulatory compliance requirements.
- Experience working in highly regulated financial services environments with strong governance and audit requirements.
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.
