LawnStarter: Analytics Engineering Manager, Data Platform & Governance
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 $750M 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.
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, and of the roadmap that makes it more trustworthy every quarter. Trust 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; every manager at LawnStarter builds. You'll start solo, building automation, writing checks, fixing what's broken, and putting processes in place that scale. Once you've landed, we will open a Lead Analytics Engineer role reporting to you.
What makes this role different:
- You're first: Governance has been everyone's side job, so what exists today is yours to reshape. Your standards become the company's standards.
- You own the roadmap, not a backlog: You discover what the business needs from data and decide what gets built, in what order, and why.
- Whole-stack ownership: From source data to pipelines to dashboards and ML models, you own trust across the entire chain.
- A live migration to shape: Lightdash is landing now. You get to set up its permissions, structure, and norms before bad habits form.
What You'll Own
- The data roadmap: Discovering what product, marketing, ops, and finance need from data, prioritizing it against platform health, and sequencing the investment.
- Data quality and freshness: Automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream.
- Data lineage and impact analysis: Maintaining a living map from production source to warehouse model to dashboard. The end-state is data contracts with engineering.
- Lightdash: Administration, workspace structure, permissions, and the rollout itself.
- The semantic layer: Extending definition and mapping to the rest of our metrics and guarding the layer against uncontrolled growth.
- Event tracking governance: Managing our governed Segment event catalog, keeping it matched to production, and evolving guardrails.
- AI data readiness: Governing what data AI tools can access and keeping the warehouse AI-legible (documented, consistent, and safe).
- Data security and privacy: Access controls, PII handling, and retention under US state privacy laws.
- The governance system: The documentation, ownership models, and review loops that keep the system running.
Problems to Solve
- Turn business needs into a data roadmap: Build the intake and prioritization process that balances stakeholder needs against platform health.
- Make the Lightdash migration a step-change: Design the structure, spaces, and permissions that allow stakeholders to self-serve without creating a dashboard-growth nightmare.
- Finish and defend the semantic layer: Extend coverage and keep the "one-metric-one-definition" rule true as the layer scales.
- Tame event-tracking entropy: Act as the dedicated owner for the governed event catalog to prevent silent degradation.
- Get ahead of breakage: Take detection from partial to comprehensive and wire it into engineering's change review process.
What Success Looks Like (Year 1)
- Zero pipeline incidents from unannounced source-data changes.
- Zero freshness incidents; stakeholders never open a stale dashboard.
- Standardized metrics: Every area of the business manages on official, well-maintained metrics and dashboards in Lightdash; Tableau and Metabase are retired.
- Governed events: Every Segment event has an owner and a standard.
- Governance as a system: Documented processes that would survive you taking a month off.
Requirements
- Governance is your craft: 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, and document as you go.
- A hands-on manager: You have experience managing output and priorities, but you never stopped building yourself. You write the SQL, debug the Airflow DAG, and configure permissions personally.
- Product-minded: You start from what the business is trying to decide and can turn vague stakeholder asks into a prioritized plan.
- Automation-first: Your instinct for any recurring check is to build a monitor, not a checklist.
- An enforcer people actually like: You can hold others to standards using clear rules and good tooling, with the spine to say no gracefully.
This Role Is NOT
- A big-team leadership role (you start solo).
- A policy or committee job (you fix things with code, config, or conversation).
- A BI analyst role (you build the platform, not the dashboards).
- A finished system to babysit (you are building, not just operating).
Tech You'll Touch
- Warehouse & pipelines: Redshift, dbt, Airflow
- Ingestion: Fivetran, plus custom Airflow pipelines
- 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 (freshness monitoring, anomaly detection, dbt lineage)
Benefits
- Base salary: $75k-$120k/year
- 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.