VP, Business Data Delivery

About the role

The VP, Business Data Delivery is responsible for delivering trusted, business-ready data products that power decision-making across Happen Bank—from Marketing, Personal Loans, Deposits, Credit Risk, Fraud, Operations, and Compliance.

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
• Drive analytics initiatives that directly support business outcomes and prepare data products for AI and machine learning use cases
• 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
• 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

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
• 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
• 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

Qualifications

• Nice to have: 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