Group Product Manager, Data & Analytics
About this role
SmartRent needs a strategic leader to turn raw telemetry, IoT, and property data into predictive models and intuitive analytics tools for the rental housing industry. Managing the enterprise data platform requires bridging the gap between business leaders, data scientists, and end users to deliver market-leading reporting and machine learning solutions.
Working closely with data engineering and design teams, the incoming group product manager will shape product vision and oversee the roadmap for enterprise intelligence. Success in the position demands translating complex stakeholder requirements into clear data specifications while balancing performance, cost, and delivery timelines.
An analytical professional with a background in data products or SaaS ecosystems will thrive here. Candidates who enjoy mentoring cross-functional teams and scaling data-informed product cultures fit the organization's collaborative approach well.
What you will do
- Define and communicate the strategic roadmap for enterprise data platforms, reporting suites, and artificial intelligence initiatives.
- Partner with data science, machine learning, and engineering teams to turn raw property data into predictive models and actionable business intelligence.
- Translate stakeholder needs into clear technical requirements, product requirement documents, and data specifications.
- Manage qualitative and quantitative research with internal teams and property operators to understand analytical workflow needs.
- Measure product success using data-driven methods, establish core telemetry metrics, and optimize product adoption.
- Lead, mentor, and coach a team of product managers while building a data-informed product culture.
- Ensure compliance with security and operational standards by embedding secure-by-design practices into product workflows.
What you need
- Hold a bachelor's degree in marketing, economics, computer science, business, or a related technical field.
- Bring a minimum of 7 years of overall experience in product management.
- Accumulate at least 3 years of experience managing data products, analytics platforms, data infrastructure, or machine learning solutions.
- Demonstrate 2 to 5 years of people management experience building and growing high-performing teams.
- Possess proven experience collaborating directly with data scientists and engineers to ship data-driven features.
- Show solid understanding of data pipelines, ETL processes, data architecture, and machine learning deployment.
- Exhibit demonstrated capabilities executing end-to-end quantitative and qualitative customer research.
Nice to have
- Proficiency in querying databases using SQL and evaluating model performance metrics.
- Experience building external-facing analytics and dashboards for enterprise SaaS customers.
- Background in proptech, multifamily, consumer IoT, or similar SaaS companies.
- Familiarity with modern data stack architectures like Snowflake, Databricks, and dbt.
- Understanding of large language models, model context protocol, and generative AI.
Benefits
- Medical, dental, vision, and life insurance with low deductibles and 75-100% employer contributions
- Flexible and generous PTO
- Competitive 401(k) with employer contributions
- Paid parental leave
- Discounted insurance plans for pets and legal services
- Employee stock purchase plan
Skills and tech stack
Questions candidates ask
Where is this role located?
Candidates in Arizona are preferred to come to the headquarters, but the company is open to applicants in other US states.
What is the salary range for this position?
The annual salary range is $155,000 to $186,267, with actual compensation depending on experience, skills, and internal equity.
What kind of technical background is expected?
Hands-on coding or data science execution is not required, but strong technical comprehension of data pipelines, ETL processes, and machine learning deployment is necessary.
What leadership duties does the job involve?
The position includes leading, mentoring, and coaching a team of product managers while building a data-informed product culture across the department.