Data Governance and Platform Manager

About LawnStarter

LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services - operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a single shared platform.

About Analytics at LawnStarter

We're a small, senior analytics team supporting the entire company - product, marketing, operations, and finance all run on the data we serve. The foundation is solid: a centralized Redshift data warehouse where all source data lands, modeled in dbt and orchestrated by Airflow, with Segment feeding event data in. You won't be stitching scattered sources together - the platform exists; your job is to make it trustworthy and keep it that way. We're mid-migration to Lightdash as our single BI platform, replacing Tableau and Metabase.

Here's the honest gap: everyone on the team today is an analyst. Data quality, tracking standards, and platform hygiene get done as side work, squeezed between analyses. Nobody wakes up thinking about them - which is exactly the job we're hiring for.

The Role

You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself.

This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build.

What makes this role different:
- You're first.  Governance has been everyone's side job, so what exists today is yours to reshape - keep what works, redesign what doesn't, and your standards become the company's standards.
- Whole-stack ownership.  Source data to pipelines to dashboards and ML models - you own trust across the entire chain, not one slice of it.
- A live migration to shape.  Lightdash is landing now. You get to set up its permissions, structure, and norms before bad habits form, instead of untangling them later.

What You'll Own
- Data quality and freshness  - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen.
- Data lineage and impact analysis  - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. The end-state is data contracts with engineering, so breaking changes get caught in their workflow, not ours.
- Lightdash  - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there.

Enablement is part of the deal - people follow standards they've been taught - and so is keeping queries fast and warehouse costs sane.
- The semantic layer  - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales.
- Event tracking governance  - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows.
- AI data readiness  - AI agents query our warehouse every day through Brain, o