Lead, Inventory Operations

Compass•Madison, NY

2d•Hybrid

About The Position

Compass International Holdings is seeking a Lead, Inventory Product, Data & Ops to ensure inventory products function as promised across all ten brands and to rebuild operational processes using AI. This role is responsible for the technical and operational backbone of listing distribution, ensuring listings are processed correctly, on time, and according to seller instructions. The position involves testing and validating product releases, building and deploying AI systems for daily processes, maintaining accurate reporting, and documenting all workstreams. The ideal candidate is AI-first, understands both business needs and technology, and can collaborate with Product and Engineering teams. This role is crucial for ensuring product reliability and enabling a small team to operate at national scale through AI automation.

Requirements

4+ years in product operations, business or technical operations, QA, strategy and operations, or a similar role in a technology-driven environment.
A true hybrid of business and technical: ability to explain product issues to brand leaders and walk engineers through reproduction steps.
Composure and credibility in high-stakes conversations with agents and sales leaders.
Hands-on experience vibe coding, stress testing, and deploying AI automations that run in production and that other people depend on (e.g., LLM APIs, agentic workflows, workflow platforms like n8n, Zapier, Make, or Workato, or Python scripting), with specific examples of processes handed to AI.
Experience designing and running structured QA or UAT programs: test plans, scenario coverage, defect tracking, and readouts.
Fluency in how product teams work: reading PRDs and technical specs, understanding APIs and data feeds conceptually, working in Jira or similar tools, and navigating release cycles.
Experience directing offshore or vendor teams, including writing clear instructions and holding a quality bar.
Strong data skills: SQL, advanced spreadsheets, and a BI tool such as Looker or Tableau.
A systems thinker who documents while building and designs for the failure case.
Experience rolling out new workflows to operational teams and getting them adopted.
Sound judgment with sensitive data in a regulated environment, including knowing when to bring in Legal or Compliance before building.
Exceptionally organized and detail oriented, with a track record of building documentation and trackers that teams and cross-functional partners rely on.
Comfortable with ambiguity and able to execute independently.

Nice To Haves

Real estate brokerage, MLS, listing syndication, or proptech experience is a strong plus.
Familiarity with listing data standards and feeds (e.g., RESO, IDX) is a plus.

Responsibilities

Own day-to-day execution of QA testing and quality validation (including secret shopping) for inventory products across the MLS, agent platform, brand websites, and portals.
Build and maintain standard testing practices: test plans, scenario libraries, pass/fail criteria, defect severity levels, and readout templates.
Coordinate offshore testing teams: scope work, write clear test instructions, set turnaround expectations, and review output against a quality bar.
Partner with Product and Engineering as new features approach release; read requirements, map scenarios and edge cases, and get tests designed before launch.
Organize test readouts: synthesize results, prioritize defects by business impact, and bring recommendations to stakeholders.
Track defects to resolution and confirm fixes in production through regression testing.
Apply AI to testing itself, from generating scenario coverage to automating repeatable checks and secret shopping.
Support technical readiness for new launches by validating products before they reach agents.
Be the team's system of record: keep test plans, results, defects, automations, SOPs, and decisions documented, current, and easy to find.
Build and maintain the trackers the team runs on (testing cycles, automation builds, open issues, requests, and cross-functional commitments).
Organize every cross-functional interaction with Product, Engineering, Data & Analytics, offshore teams, and brand stakeholders.
Document every automation so others can understand, audit, maintain, and improve it.
Keep a clean record of product behavior, known issues, and MLS rule interpretations.
Set documentation and tracking standards for the team.
Help build, test, and deploy AI automations for prioritized processes, so AI owns the work end to end.
Prototype quickly, stress test before anything goes live (edge cases, bad inputs, volume, failure scenarios).
Harden prototypes into systems that run daily in production.
Set up monitoring for each automation: expected outputs, accuracy checks, error handling, alerting, and escalation paths.
Measure each automation's impact in hours returned, error rates, and turnaround time.
Build governance in from the start: partner with Legal, Compliance, Privacy, and Security.
Train support, brand-facing, and operations teams on new workflows.
Manage the change from manual to automated work: define exception review, retire old processes, and track adoption.
Partner with Data & Analytics to define reporting requirements for inventory products and programs.
Test and validate report and dashboard outputs before they are used for decisions.
Trace discrepancies between systems (MLS feeds, agent platform, brand websites, portals) to root cause.
Monitor progress to goal on inventory metrics and flag risks to the Director early.
Prepare and validate the inventory metrics and updates provided to Investor Relations.
Run manual validations where data or automation can't yet be trusted, then turn recurring checks into automated ones.
Help stand up new product offerings quickly, including testing, SOPs, tracking, and reporting.
Keep the SOPs governing how listings are entered, configured, distributed, and corrected current.
Handle escalations directly from agents and sales leaders with urgency and clarity.
Diagnose technical issues behind escalations by reproducing the problem, gathering evidence, and bringing a root cause hypothesis to Product and Engineering.
Represent the team and its brand in every field interaction.
Be the team's working expert on how inventory products and automations behave in practice.