Vice President, Corporate Operational Risk

We are seeking a team member to lead the design and delivery of Operational Risk's data and analytics ecosystem by combining deep data engineering expertise with risk, governance, and reporting knowledge to enable data-driven risk intelligence across the organization.

In this role, you’ll make an impact in the following ways:

• Serve as a subject matter expert for Operational Risk Reporting & Analytics, providing strategic data leadership and technical direction.

• Design and manage secure, scalable, and resilient data architectures that support Operational Risk reporting, analytics, governance, and regulatory requirements.

• Build and optimize end-to-end ETL/ELT pipelines using AI, Python and modern data engineering frameworks to automate ingestion and integration of data from multiple internal and external sources.

• Lead advanced data transformation and modeling using dbt and Snowflake, creating trusted, high-quality datasets that support enterprise risk reporting and analytics.

• Partner with Operational Risk, Engineering, Audit, Technology, and Business stakeholders to translate complex business requirements into scalable data and analytics solutions.

• Deliver business intelligence, reporting, dashboards, and self-service analytics capabilities that provide actionable risk insights to management, committees, and regulatory stakeholders.

• Establish and maintain robust data governance, quality, lineage, stewardship, and control frameworks to ensure data integrity, transparency, and compliance.

• Drive Agile delivery, automation, innovation, and continuous improvement, leveraging modern engineering practices, AI-enabled solutions, CI/CD pipelines, and emerging technologies.

• Transform operational risk data into proactive risk intelligence that supports risk identification, trend analysis, decision-making, and broader Operational Risk Management objectives.

To be successful in this role, we’re seeking the following:

• Bachelor’s degree in computer science, Information Systems, or a related field, or an equivalent combination of education and experience.

• 6+ years of experience in Data Engineering, ETL/ELT development, and Data Analytics

• Technical Stack Mastery:

  • AI: experience building and using AI agents

  • Snowflake: Deep understanding of cloud data warehouse architecture, virtual warehouses, performance tuning, and role-based access control.

  • Python: Advanced programming skills for data manipulation, API integration, and automation.

  • dbt: Proven track record of using dbt for managing staging/mart layers, DAGs, and automated testing in a production environment.

  • SQL: Expert-level ability to write complex, performant queries and analytical functions.

  • Streamlit: Hands-on experience building lightweight, interactive Python UI applications.

  • Power BI: Hands-on experience on building basic reports and dashboards

• Core Concepts: Strong foundational knowledge of data warehouse dimensional modeling, data analytics, and robust data governance principles (data quality, stewardship, and compliance).

• Domain Knowledge: Previous experience working in Risk Management, specifically Non-Financial Operational Risk, Compliance, or Audits is highly preferred.

• Soft Skills: Exceptional cognitive and problem-solving skills, leadership skill, meticulous attention to detail, and a demonstrated eagerness to learn and adopt emerging technologies. Excellent communication skills to bridge the gap between technical teams and business leadership.

• Additional Tools: Experience with AWS, Azure products and BI tools is a strong plus.