We’re Changing the PropTech Industry
We’re a profitable, growth-stage company building industry-leading martech and data products for the PropTech space. Our focus is on our category-leading AI and data SaaS products with triple-digit growth rates.
We’re a fully remote team of 100+ spread across the U.S. from coast to coast. We operate on a strict no a**holes policy and are proud to have built a community of highly performant people that take our work seriously, but not ourselves. And, we're looking for exceptional people to help further accelerate our growth.
While we’ve raised $30MM+ to date from some of the world’s best investors, we’re profitable with a strong balance sheet and an indefinite runway. We pride ourselves on achieving rapid growth without having to incinerate capital.
About Our Products
ApartmentIQ is the all-in-one data platform powering smarter decisions across multifamily—covering 40M units with daily property and market intelligence used for surveys, research, and revenue management. We're trusted by 65% of the NMHC Top 50. Our sister company, MavenAI, delivers AI-powered marketing automation built by industry experts and integrated directly with property management systems, serving over 2M units.
We’re looking for a Technical Staff Product Manager to own ApartmentIQ’s data platform, the foundation behind the market intelligence multifamily operators rely on every day. This role is for someone with a data science or analytics background who is as comfortable writing SQL and digging into a pipeline as they are setting product direction.
You’ll set the vision and roadmap for how we ingest, model, validate, and deliver data, working closely with data engineering, data science, and product teams. When something looks off, you’ll dig in yourself: run the analysis, trace the issue to its root cause, and turn what you find into fixes and platform improvements. You’ll also work directly with customers to learn how they use our data,
Responsibilities
- Own the strategy and roadmap for the data platform, including ingestion, data models, data quality, and delivery to internal and customer facing products
- Lead root cause analysis on data quality issues, anomalies, and metric discrepancies, and work with engineering on fixes that last
- Define data quality standards, SLAs, and monitoring so issues are caught before they reach customers
- Partner with data engineering and data science to scope technical requirements, weigh tradeoffs, and prioritize the backlog
