About The Role

CIB Controls Management is at the forefront of a rapidly evolving landscape, where regulatory expectations, operational complexity, and digital transformation are reshaping how risk and controls are managed. As one of the industry's largest and most influential institutions, JPMorganChase has a unique opportunity to establish new standards for data-driven controls management, enterprise reporting, and AI-enabled decisioning.

This Vice President role within the Corporate & Investment Bank (CIB) Controls Management Tech & Tooling team is responsible for driving the design, delivery, and evolution of the Centralized Control Data Store (DRIFT), a strategic data platform underpinning Controls Assessment Transformation (CAT). Operating at the intersection of business, data, and engineering, the role will lead the integration of control data across approximately 30-40 upstream systems into a centralized Snowflake-based ecosystem, enabling scalable analytics, enterprise reporting, thematic insights, and AI/ML-driven controls capabilities.

As a Lead Data Engineer within the CAT operating model, you will partner closely with Product Managers, Engineering teams, Data organizations, and senior stakeholders to translate strategic requirements into scalable data solutions. You will drive the development of robust data pipelines, trusted data products, and modern engineering practices that enhance data quality, operational resilience, and self-service analytics. The role offers the opportunity to shape next-generation data capabilities that accelerate controls transformation, improve decision quality, and support AI-enabled innovation across Corporate & Investment Bank (CIB) Controls.

Key Responsibilities

Design and develop scalable data pipelines to ingest, transform, and manage data from multiple structured and semi-structured sources within Snowflake.
Build and optimize data models, ETL/ELT processes, and reusable engineering components to support reporting, analytics, and AI/ML use cases.
Implement data quality controls, validation frameworks, and monitoring capabilities to ensure trusted and reliable datasets.
Collaborate with upstream application teams and data providers to onboard new data sources, resolve data issues, and support ongoing platform enhancements.
Partner with analytics, reporting, and controls teams to deliver consumption-ready data products that meet business requirements.
Support platform stability, performance tuning, operational monitoring, and production issue resolution while contributing to engineering best practices.
Provide technical leadership and mentorship to junior engineers, promoting high-quality design, coding, testing, and documentation standards.

Required Qualifications

8+ years of data engineering experience with significant hands-on development experience in Snowflake-based environments.
Strong expertise in Snowflake, including Snowpipe, Tasks, Streams, SQL, and modern data modeling practices.
Strong programming skills in Python and experience building scalable ETL/ELT solutions.
Demonstrated hands-on expertise in Snowflake, SQL, Python, and modern data engineering practices.
Exposure to vector databases, RAG pipelines, and AI/LLM-enabled data architectures is a plus.
Experience integrating data from multiple upstream systems and supporting large-scale data transformation and reporting workloads.
Solid understanding of data governance, data quality, metadata management, and operational support processes.
Strong problem-solving, stakeholder management, and communication skills with the ability to work effectively across business and technology teams.
Experience mentoring engineers and leading technical workstreams within agile delivery teams.