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.
- 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.
