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Salary: USD 75,000 - 120,000 / annual
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $750M in annual bookings. The company is expanding beyond lawn care to become a one-stop shop for all home services across three brands (LawnStarter, Lawn Love, Home Gnome) on a shared platform.
You will be the first person at LawnStarter dedicated to data governance—the owner of whether the company's data can be trusted and the roadmap that makes it more trustworthy every quarter. The analytics team today is small and senior, supporting product, marketing, operations, and finance. The foundation is solid: a centralized Redshift data warehouse with dbt modeling and Airflow orchestration, Segment feeding event data, and a mid-migration to Lightdash as the single BI platform replacing Tableau and Metabase. The honest gap: everyone on the team is an analyst, and data quality, tracking standards, and platform hygiene get done as side work. This role owns those full-time.
You'll start solo, hands-on, building automation, writing checks, fixing what's broken, and putting processes in place that scale. Once you've landed, a Lead Analytics Engineer role will report to you, and the function grows as scope demands.
Key ownership areas:
- Data roadmap: discovering what product, marketing, ops, and finance need from data; prioritizing against platform health; sequencing investment and presenting/defending the plan as the business moves.
- 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.
- Data lineage and impact analysis: a living map from production source to warehouse model to dashboard; assessing downstream impact of proposed production changes before they ship; moving toward data contracts with engineering so breaking changes get caught in their workflow, not yours.
- Lightdash administration: workspace structure, permissions, rollout, enablement, and keeping queries fast and warehouse costs sane while giving the company self-serve autonomy.
- Semantic layer: extending definition and mapping to all metrics (currently shipped for critical metrics only); guarding against uncontrolled growth as it scales.
- Event tracking governance: reviewing new Segment events against standards; keeping the catalog matched to what production actually sends; evolving guardrails (naming, property dictionary, drift detection) as tracking grows.
- AI data readiness: governing what data AI tools can access; keeping the warehouse AI-legible (documented, consistent, safe for agents to query and get the right answer).
- Data security and privacy: access controls, PII handling and retention under US state privacy laws, periodic reviews of who and which AI tools can see what.
- Governance system itself: documentation, ownership models, and review loops that keep everything running without heroics.
Year 1 success means: zero pipeline incidents from unannounced source-data changes (lineage and automation catch them first); zero freshness incidents; every area of the business managing on official, well-maintained metrics and dashboards in Lightdash with a fully mapped semantic layer; every Segment event with an owner and standard; governance running as a documented system that would survive you taking a month off.
Tech stack: Redshift, dbt, Airflow, Fivetran, Segment, Lightdash (primary), Tableau and Metabase (sunsetting), Claude Code, Codex, Brain (internal AI toolkit), and an AI-powered Analytics Engineer agent you'll scale up.
REQUIREMENTS:
- Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy and can't leave a broken naming convention alone.
- AI-native: you use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go. You see AI agents consuming data daily and understand making the warehouse safe and legible for them is part of governance.
- Hands-on manager: you've been accountable for other people's output, allocating their time, owning their priorities, and you never stopped building yourself. You write SQL, debug Airflow DAGs, and configure permissions personally.
- Product-minded: you start from what the business is trying to decide, not from what the pipeline does, and can turn a vague stakeholder ask 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'll hold engineers and analysts you don't manage to standards with clear rules, good tooling that makes compliance easy, and the spine to say no gracefully.
- Hands-on depth in the warehouse/pipeline layer and credible experience keeping a BI tool and tracking plan healthy at company scale.
