Data Governance – Platform Manager

Role Overview

• 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.

Tech Stack

• Airflow
• SQL

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.