VP, Business Data Delivery

LendingClub•San Francisco, CA

2d•$240,000 - $270,000•Hybrid

About The Position

The VP, Business Data Delivery is responsible for delivering trusted, business-ready data products that power decision-making across LendingClub—from Marketing, Personal Loans, Deposits, Credit Risk, Fraud, Operations, and Compliance. This leader owns the data engineering, analytics engineering, data product management, and customer data delivery capabilities that translate business priorities into measurable outcomes. Partnering closely with business leaders and the VP, Data Platforms, this role defines how customer, product, and operational data is transformed into actionable insights, analytics, AI-enabled experiences, and decisioning capabilities. Driven by LendingClub’s mission of improving the financial health of everyday Americans, the VP will build a data-driven culture, shape the company’s customer data strategy, and lead the teams responsible for turning data into measurable business value.

Requirements

13+ years of data engineering, analytics engineering, data product management, or related technology experience, including 10+ years leading and developing teams across data engineering, analytics, and data product management
5+ years leading large-scale data products, customer data platforms, analytics organizations, or business-facing data delivery functions in a public cloud environment
Demonstrated experience building data products and services that support customer experiences, marketing activation, credit decisioning, fraud detection, operations, and business intelligence
Experience leading customer data delivery, Customer Data Platform programs, identity resolution capabilities, and downstream activation use cases
Strong understanding of modern data architectures, customer data platforms, analytics ecosystems, and data product operating models
Experience leading Data Engineering, Analytics Engineering, Data Product Management, or Data Science organizations
Ability to translate business objectives into data product strategies and measurable outcomes
Strong executive communication skills with the ability to influence business, technology, and regulatory stakeholders
Experience promoting data literacy and building data-driven cultures across technical and non-technical organizations
Strong understanding of modern data tooling and ecosystems, including lakehouse platforms (Databricks preferred), transformation tooling (dbt), BI platforms (Tableau), and cloud-native data architectures
Hands-on familiarity with advanced analytics, machine learning, and AI-enabled business capabilities
Experience operating within highly regulated industries such as financial services, healthcare, or similar environments
Ability to deliver value quickly and incrementally while balancing strategic objectives and business priorities
Bachelor's degree or higher; or equivalent combination of education and work experience

Nice To Haves

Experience working in both high-growth organizations and large-scale enterprises
Understanding of consumer financial products, personal financial management, lending, deposits, fraud, or risk domains
Experience building data products for credit risk, fraud, collections strategy, marketing, or operations analytics
Experience with AWS and modern cloud-native data ecosystems
Experience leading enterprise AI enablement initiatives and modern data product transformations
Proven ability to attract, hire, and develop exceptional technical and product talent

Responsibilities

Define and execute the strategy and roadmap for business-facing data products that support Marketing, Lending, Credit Risk, Fraud, Operations, Deposits, and Compliance
Lead the delivery organization across Data Engineering, Analytics Engineering, Data Product Management, and Data Science
Partner with business leaders to translate strategic priorities into scalable data products and measurable outcomes
Establish operating models that ensure data investments align with business value, adoption, and business outcomes
Lead the Data Product Management practice, including intake management, prioritization frameworks, delivery governance, and the operating model between business stakeholders and delivery teams
Own the end-to-end customer data product strategy, including customer, product, and behavioral data integration
Deliver trusted customer data capabilities that power marketing activation, personalization, credit and fraud decisioning, collections strategy, and operational intelligence
Lead Customer Data Platform (CDP) initiatives, identity resolution capabilities, and downstream activation across business functions
Partner with business teams to evolve the customer data strategy and ensure a unified customer view across the enterprise
Define the strategy for business-facing analytics, AI enablement, and self-service data products
Lead analytics initiatives that directly support business outcomes and prepare data products for AI and machine learning use cases
Drive adoption of AI-assisted data discovery, automated insight generation, and GenAI-powered self-service capabilities
Enable advanced analytics and machine learning through high-quality, business-ready data products that accelerate decision-making
Establish standards for data quality, certification, testing, QA, and governance throughout the data product lifecycle
Ensure business confidence in data products through robust validation, monitoring, and delivery processes
Partner closely with the VP, Data Platforms to align delivery capabilities with platform strategy, architecture, and engineering standards
Promote strong data practices and ensure data requirements are incorporated early in product and business planning processes
Build a data-driven culture by embedding data product management and analytics capabilities close to the business and holding teams accountable for outcomes, not output
Lead and develop high-performing teams across Data Engineering, Analytics Engineering, Data Product Management, and Data Science
Foster a culture of accountability, innovation, collaboration, and continuous improvement
Attract, coach, mentor, and retain exceptional technical, analytical, and product talent
Serve as a trusted advisor to executive leadership on data strategy, customer data, analytics, and AI opportunities