Lead Product Data Analyst

About LawnStarter

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. We're expanding beyond lawn care to become the one-stop shop for all home services, and we're investing in the next generation of our platform to get there.

About the Team

We're a high-leverage team of Product Data Analysts embedded across the business, owning the semantic layer and the metrics everyone trusts. We turn "I have a hunch" into "here's what actually happened," and we're the reason teams across the company can make calls on evidence instead of instinct. This role brings dedicated analytical firepower to the Pro (supply) side of that work.

Requirements

The Role

As a Lead Product Data Analyst, you'll directly impact our results through insights and reports. You'll work closely with product managers, researchers, and other business stakeholders, helping with prioritization, assessments, and business recommendations. Alongside the rest of the Analytics team, it's your responsibility to nurture the data-driven culture within the company, making data easier to consume, whether through interactive reports, easy-to-use datasets, documentation, or training.

You'll work with the autonomy of a Lead: setting your own standards, working independently, and acting as a trusted thought partner rather than an order-taker. That title isn't about managing people, there's no team attached to it. It's about the bar you hold your own analysis to, and the bar you help everyone around you reach.

This role leans toward the Pro (supply) side of our marketplace, though the exact focus flexes with where the business needs the most insight.

What You'll Own

Modeling & Analysis: A marketplace is a complex system, with many moving parts and often contradicting signals. That creates an exciting pool of opportunities to find gaps, insights, and optimizations. Analyses range from a simple A/B test to a multivariate model on retention or ETA, backed by advanced SQL and intermediate Python or R.

Reporting: A complex system produces a high number of metrics worth tracking. A dashboard is only as good as our trust that it's correct and current. You'll understand the needs of the teams you work with and help create and maintain the reporting system, keeping it organized and easy to act on.

Analytics Engineering: Occasionally you'll work in the inner layers of the Data Warehouse to provide clean, documented datasets that power our reports and end users. We use dbt for transformation, so SQL is a must.

Problems to Solve

Metrics nobody fully trusts. Different teams cite different numbers for the same thing, and no one's quite sure which is current. Untangling that and giving the business one number it can stand behind is core to the job.

Analysis that ships but doesn't move anything. A technically correct answer that doesn't change a single decision is still a failure. Getting a stakeholder to actually act on what you found is the hard part, not the SQL.

Data that's hard for anyone but an analyst to touch. If every question requires filing a ticket and waiting on you, you haven't built a data-driven culture, you've built a bottleneck.

A system with too many moving parts and not enough signal. Supply, demand, pricing, and service quality all interact. Teasing out what's actually driving a metric versus what's noise is a real analytical problem, not a formality.

What Success Looks Like (Year 1)

The metrics teams rely on daily are trusted, documented, and current, no one's quietly keeping a shadow spreadsheet because they don't trust the dashboard.

Routine questions are self-serve: stakeholders find their own answers in existing reports instead of pinging you for a one-off pull.

You can name specific decisions your analysis changed, not just analyses you delivered.

The datasets and models you've built in dbt are clean and documented enough that other analysts build on them without redoing your work.

Who You Are