LawnStarter: Data Governance & Platform Manager
Headquarters: Brazil
URL: http://lawnstarter.com
About LawnStarter
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M 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. 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. 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, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query.
- Data security and privacy: Access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what.
- The governance system itself: The documentation, ownership models, and review loops that keep all of the above running without heroics.
Problems to Solve
- Make the Lightdash migration a step-change: You'll design the structure - spaces, permissions, certification, naming - that lets stakeholders self-serve at the speed the company needs without creating an uncontrolled dashboard-growth nightmare.
- Finish and defend the semantic layer: You'll own extending coverage and keeping one-metric-one-definition true as the layer scales.
- Tame event-tracking entropy: You will be the dedicated owner who holds every new event to the standard, keeps the catalog matched to what production actually sends, and evolves the guardrails as tracking grows.
- Get ahead of breakage: You'll take detection from partial to comprehensive, extend lineage beyond dbt, and wire it into engineering's change review so a proposed production change comes with a downstream impact assessment.
What Success Looks Like (Year 1)
- Zero pipeline incidents from unannounced source-data changes.
- Zero freshness incidents - stakeholders never open a stale dashboard.
- Every area of the business manages on official, well-maintained metrics and dashboards; Tableau and Metabase are retired.
- Every Segment event has an owner and a standard - new events ship compliant, and degradation gets caught automatically.
- Governance runs as a system - documented processes that would survive you taking a month off.
Who You Are
- Governance is your craft, not your chore: You genuinely enjoy making data systems trustworthy and tidy.
- AI-native: You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go.
- A hands-on senior operator: You write the SQL, debug the Airflow DAG, and configure the permissions yourself.
- Automation-first: Your instinct for any recurring check is to build a monitor, not a checklist.
- An enforcer people actually like: You'll hold engineers and analysts you don't manage to standards - which takes clear rules, good tooling, and the spine to say no gracefully.
This Role Is NOT
- A people-management role - yet: You'll work alone for a while.
- A policy or committee job: There are no governance councils to chair. When something's broken, you fix it - with code, config, or a conversation.
- A BI analyst role: You won't spend your days building dashboards for stakeholders.
- A finished system to babysit: Much of this doesn't exist yet.
Tech You'll Touch
- Warehouse & pipelines: Redshift, dbt, Airflow
- Event tracking: Segment
- BI: Lightdash (primary), Tableau and Metabase (sunsetting)
- AI tooling: Claude Code, Codex, Brain (internal AI toolkit)
- Observability: AI-powered Analytics Engineer agent, plus quality and impact tooling you'll add
Compensation & Benefits
- Base salary: $75k-$100k/year
- Equity: We want you to own a piece of the company's success.
- Fully remote: Async collaboration is the norm.
- Flexible PTO: We focus on results. Take what you need.
LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics.
To apply: https://weworkremotely.com/remote-jobs/lawnstarter-data-governance-platform-manager