Data Governance – Platform Manager

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LawnStarter

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2013

👥 B2C

🏪 Marketplace

🏠 Real Estate

💰 $10.5M Venture Round on 2019-11

B2C • Marketplace • Real Estate

LawnStarter is a service platform that provides affordable lawn care services at the click of a button. With fast online ordering, quality service, and a user-friendly app, LawnStarter allows customers to easily schedule and manage lawn maintenance services. These include lawn mowing, fertilization, bush trimming, and weeding. The company partners with local professionals to ensure reliable and insured services while supporting small businesses. LawnStarter offers its services across numerous locations in the United States, aiding customers in keeping their lawns beautiful and well-maintained.

📋 Description

• 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. • 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, 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 and get the right answer. • 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.

🎯 Requirements

• Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy - you're the person who can't leave a broken naming convention alone. This is unlikely to be a good fit if you see governance as a stepping stone to "real" analytics work. • AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go - one person covering ground that used to take a team. You also see the reverse direction: AI agents consume our data daily, and making the warehouse safe and legible for them is part of governance now. This is unlikely to be a good fit if you're skeptical of AI tools or prefer to do everything manually. • A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself - seniority here means judgment and speed, not delegation. This is unlikely to be a good fit if your last few years were spent directing others and you'd need a team to execute. • Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. This is unlikely to be a good fit if your quality practice depends on manual review and discipline. • An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards - which takes clear rules, good tooling that makes compliance easy, and the spine to say no gracefully. This is unlikely to be a good fit if you avoid friction or, at the other extreme, enjoy being the department of no.

🏖️ Benefits

• Base salary: $75k–$100k/year • Equity: The whole company makes decisions on the data you'll guard. When data trust goes up, decision quality, and company value, go up with it. We want you to own a piece of that. • Fully remote: This work needs deep focus, building monitors, untangling pipelines, and we trust you to manage your environment. Async collaboration is the norm. • Flexible PTO: We focus on results. Take what you need.