Auction Jobs in Riverside, CA (NOW HIRING)

Role Summary
The VP of Data Intake Operations provides end-to-end leadership and accountability for all data intake, data management, data quality, and data sourcing operations supporting Auction.com listings through auction readiness. This role ensures that teams executing Auction Readiness I–III responsibilities are properly structured, enabled, governed, and scaled to meet business, compliance, and consumer-facing data requirements.

The VP owns the operating model, quality standards, escalation framework, and technology strategy, including AI, that underpin accurate, complete, and timely data intake, validation, enrichment, and platform accuracy across Auction Readiness-level work.
As the platform grows, this role will evolve into a broader enterprise data enablement and merchandising function, expanding its scope from operational data intake oversight to enterprise-wide data capability, content accuracy, merchandising readiness, and scalable platform enablement. The role will also be responsible for identifying, evaluating, and operationalizing sources for data critical to the business, including photos, property DNA, pricing, and auction day information.

Core Responsibilities
Operational Ownership & Accountability
•    Own the full lifecycle of data intake, data ingestion, validation, enrichment, quality control, and auction readiness across all Auction Readiness levels
•    Ensure compliance with state statutes, investor guidelines, client requirements, and internal data governance standards as executed by Auction Readiness I–III roles
•    Establish quality thresholds, audit standards, and performance expectations to ensure accuracy across Salesforce and Auction.com platforms
•    Act as the senior escalation point for systemic data issues, complex exceptions, recurring defects, and platform accuracy risks identified by the team
Team Structure, Enablement & Leadership
•    Lead and develop Managers and Auction Readiness team members responsible for frontline execution
•    Ensure Auction Readiness I–III roles are properly trained, supported, and aligned to standardized intake, data management, and data quality workflows
•    Define clear ownership models for intake, validation, reporting, escalation, exception handling, and data stewardship
•    Partner with HR and leadership to build sustainable career paths and role clarity within the team

Process Governance & Scale
•    Design and govern scalable data intake and data management processes that support a high-volume, rapidly changing data landscape
•    Ensure SOPs, documentation, data standards, and training materials remain current as processes evolve
•    Identify workflow gaps, handoff risks, data integrity issues, and inefficiencies impacting Auction Readiness execution
•    Standardize escalation paths so complex data exceptions are resolved quickly without disrupting production
Technology, Data & Automation Strategy
•    Partner with Technology and Salesforce teams to improve system-to-system data flow, data intake controls, monitoring, and reporting
•    Oversee implementation of AI-enabled and automated solutions that reduce manual effort and improve data accuracy at the Auction Readiness level
•    Ensure reporting, dashboards, data validation tools, and exception monitoring meet operational and client needs
•    Evaluate vendor and outsourcing solutions where appropriate to support scale, data quality, and operational resiliency

Cross Functional & Strategic Leadership
•    Partner closely with Seller-Facing, Technology, Salesforce, and Operations teams to improve intake quality, data flow, and platform accuracy
•    Support organizational consolidation and operating model changes that strengthen data ownership and operational accountability
•    Identify and develop trusted internal and external sources for business-critical data, including photos, property DNA, pricing, and auction day information
•    Provide leadership visibility into risks, trends, and performance metrics impacting intake, data management, and data quality