SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 75,000 - 100,000 / annual
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings and two consecutive years of profitability. The company is expanding beyond lawn care to become a one-stop shop for all home services and investing in the next generation of its platform.
As a Lead Product Data Analyst, you will directly impact business results through insights and reports. You'll work closely with product managers, researchers, and business stakeholders to support prioritization, assessments, and business recommendations. You'll help nurture a data-driven culture by making data easier to consume through interactive reports, datasets, documentation, and training.
This is an individual-contributor role with the autonomy of a Lead—you set your own standards, work independently, and act as a trusted thought partner rather than an order-taker. There is no team attached to this role; "Lead" describes the bar you hold yourself and others to.
Key responsibilities include:
**Modeling & Analysis**: Navigate a complex marketplace system with many moving parts and often contradicting signals. Conduct analyses ranging from simple A/B tests to multivariate models on retention or ETA, backed by advanced SQL and intermediate Python or R.
**Reporting**: Create and maintain reporting systems that teams trust and can act on. Ensure metrics are correct, current, organized, and easy to understand.
**Analytics Engineering**: Work in the data warehouse to provide clean, documented datasets that power reports and end users. Use dbt for transformation.
Key problems you'll solve include: establishing one trusted set of metrics across teams, ensuring analysis actually drives decisions (not just delivering technically correct answers), building self-serve data access so stakeholders don't need to file tickets, and teasing out signal from noise in a complex system where supply, demand, pricing, and service quality all interact.
Success in Year 1 means: metrics teams rely on are trusted and documented; routine questions are self-serve; you can name specific decisions your analysis changed; and datasets and models are clean enough for other analysts to build on.
**Requirements:**
- AI-native: daily use of AI tools (Claude, ChatGPT, Copilot, etc.) to move faster—drafting and debugging SQL and dbt models, scripting analysis, and shaping reports. Skepticism of AI tools or preference for doing everything by hand is a poor fit.
- Learning mindset: take pride in understanding problems deeply and asking the right questions before reaching for answers. Avoid applying preconceived processes without first learning the business.
- Sets the bar: work autonomously, hold your own analysis to high standards, and raise the bar for people around you. Product managers and stakeholders should see you as a trusted thought partner.
- Team player: work alongside exceptional people with intellectual honesty and trust. Value reaching the right answer together over being right.
- Business focus: care deeply about understanding business needs and how analysis connects to product, customers, and financials. Understand how metrics drill down from highest to lowest level.
- Bias for action: avoid perfectionism and getting tied up in knots. Embrace constraints and scrappiness; prefer progress over perfect solutions.
- Technical skills: advanced SQL and dbt required; intermediate Python or R for statistical analysis; familiarity with Lightdash or similar BI tools.
- English resume required for application consideration.
