Lead Product Data Analyst

Lead Product Data Analyst

LawnStarter · Mexico (Remote) · Full-time · Posted 2026-11-10

Salary: USD 75,000–100,000

Workplace: remote

Department: Analytics

Description

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

AI-Native: You use AI tools (Claude, ChatGPT, Copilot, and similar) daily to move faster: drafting and debugging SQL and dbt models, scripting analysis, and shaping reports, and you keep experimenting with new capabilities as they show up. This is unlikely to be a good fit if you're skeptical of AI tools or prefer to do everything by hand.

Learning Mindset: You take pride in understanding problems deeply and asking the right questions before reaching for an answer. This is unlikely to be a good fit if you have a preconceived system of processes and methods and plan on just applying them without first learning all the ways our business is unique.

Sets the Bar: As a Lead, you work autonomously, hold your own analysis to a high standard, and raise the bar for the people around you, whether or not they report to you. Product managers and stakeholders should see you as a trusted thought partner, not an order-taker. This is unlikely to be a good fit if you want a title and a team before you're willing to raise everyone else's standards.

Team Player: You are ready to work alongside exceptional people, helping them achieve great results. You create an environment where people are excited to work with you daily, with intellectual honesty and trust. This is unlikely to be a good fit if you value being right over reaching the right answer together.

Business Focus: You care deeply about understanding business needs and how your analysis connects with our product, customers, and financials. You can envision how metrics drill down from the highest to the lowest level, identifying what needs to be analyzed or reported on at each. This is unlikely to be a good fit if you're happiest doing analysis for its own sake, disconnected from a decision it will drive.

Bias for Action: You understand that despite your careful approach to understanding problems, you actively avoid being a perfectionist or getting tied up in knots. You have a bias for action to make progress, and you enjoy being scrappy, with constraints that enthrall you. This is unlikely to be a good fit if you, by default, like building full solutions from the get-go.

This Role Is NOT

• A ticket queue. You're not here to pull numbers on demand, you decide what's worth measuring and how to measure it.
• A dashboard-admin seat. Maintaining BI tooling is part of the job, not the point of it. The point is the insight the dashboard delivers.
• A people-management role. This is an individual-contributor seat. "Lead" describes the bar you hold yourself and others to, not a team you manage.
• A role that waits for perfectly clean data. If you need data handed to you pre-cleaned before you can start, this isn't the right seat, you're expected to get your hands into the warehouse.

Tools of the Trade

SQL and dbt for transformation and modeling, Python or R for the occasional statistical deep dive, and Lightdash as our primary BI layer for dashboards and self-serve reporting.

Benefits

• Base salary: $75,000–$100,000 USD annually.
• AI tooling provided: The Claude routines already running pieces of our experimentation process are yours to extend, not a side project you have to justify.
• Fully remote: This is deep-focus analytical work with US-facing partners. We hire the best analyst regardless of city and trust you to manage your environment and overlap hours.
• Flexible PTO: Measured on outcomes, not hours logged.

VERY IMPORTANT REMINDER: Please upload your English resume. Applications without it will not be considered.

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