CURRENT EMPLOYEES OF LENDINGCLUB: PLEASE APPLY VIA YOUR INTERNAL WORKDAY ACCOUNT

LendingClub Corporation (NYSE: LC) is the parent company of LendingClub Bank, National Association, Member FDIC. We are the leading digital marketplace bank in the U.S., having helped our nearly 5 million members secure over $90 billion in loans to refinance high-cost debt and achieve their financial goals. Members today have mobile-first access to a growing range of products and services designed to work seamlessly together to deliver value in new ways. Everyone deserves a better financial future, and our team is committed to making that a reality. Join the Club!

PREFERRED QUALIFICATIONS

• Experience designing or operating data mesh or domain-driven data platforms
• Exposure to ML Ops, feature stores, or real-time analytics systems
• Prior experience in fintech, ads, gaming, or other high-scale consumer technology environments
• Track record of leading cloud migrations or large-scale platform modernization initiatives
• Vendor selection and management experience across data tooling ecosystem

TIME ZONE REQUIREMENTS

Primarily PT
While the position will primarily work local hours, LendingClub is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary.

TRAVEL REQUIREMENTS

As needed travel to LendingClub offices and/or other locations, as needed.

ABOUT THE ROLE

We are building a modern data platform team focused on enabling scalable, secure, and self-service infrastructure that empowers business and product teams to focus on insights-not plumbing. As the Sr Manager, Data Platform Engineering, you will lead the strategy, architecture, and execution of our data platform initiatives, delivering foundational capabilities that power analytics, real-time decision-making, and AI/ML innovation across the company.
Your team will own the end-to-end delivery of platform services using best-in-class tools like FiveTran, Airbyte, DLT, Databricks, Spark, SQL, and Snowflake-abstracting complexity, accelerating data product development, and ensuring trust, governance, and observability throughout the data lifecycle. This is a high-impact leadership role with executive visibility, shaping not only our data platform but also our broader data vision.

WHAT YOU’LL DO

• Lead platform strategy & execution: Design and scale a modern data platform that supports ingestion, transformation, quality, cataloging, governance, access, and observability across diverse data domains
• Deliver trusted, production-grade data: Implement robust data quality, monitoring, and observability frameworks to ensure transparency, reliability, and rapid issue resolution
• Accelerate data-driven outcomes: Partner with product, analytics, data science, and business stakeholders to deliver platform capabilities that unlock insights, real-time decision-making, and AI/ML adoption
• Drive operational excellence: Implement CI/CD, infrastructure-as-code, and cost-optimization practices to deliver secure, scalable, and efficient platform services
• Champion governance & compliance: Establish access controls, data cataloging, and compliance processes in partnership with InfoSec and regulatory teams
• Grow and inspire a world-class team: Recruit, mentor, and coach high-performing engineers while fostering a culture of innovation, ownership, and technical excellence
• Shape the future of data: Stay ahead of industry trends in streaming, AI/ML enablement, and data mesh principles, incorporating them into the long-term roadmap
• Optimize pipelines & workflows: Optimize data pipelines and workflows for performance, cost, and reliability, leveraging tools like Airflow or Dagster, dbt, Spark, and Databricks
• Accelerate development cycles: Standardize dbt project templates and CI/CD workflows to improve code quality, developer velocity, and operational resilience
• Build self-service frameworks: Deliver tooling, frameworks, and developer portals that reduce operational overhead and enable faster, more autonomous feature delivery
• Ensure best practices in data modeling: Drive standards for ingestion, transformation, and governed data modeling, ensuring scalability and performance across millions of records daily