Analytics Engineering Manager, Data Platform & Governance

About LawnStarter

LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M 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, 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. The roadmap means sitting with product, marketing, ops, and finance to understand what the business needs from data, turning that into priorities for the platform, and sequencing the work, yours and, soon, your team's.

This is a hands-on role, every manager at LawnStarter builds, and this one is no exception. You'll start solo, with the Analytics team around you: building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. Once you've landed, we open a Lead Analytics Engineer role reporting to you, you'll help choose them, and the function grows from there as scope demands.

• 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.
• You own the roadmap, not a backlog. Nobody hands you requirements, you discover what the business needs from data and decide what gets built, in what order, and why.
• 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

• The data roadmap - discovering what product, marketing, ops, and finance need from data, prioritizing it against platform health, and sequencing the investment. You'll present it, defend it, and re-plan it 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 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.

Problems to Solve

Who You Are

• 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 manager. You've been accountable for other people's output, allocating their time, owning their priorities, and you never stopped building yourself. You write the SQL, debug the Airflow DAG, and configure the permissions personally. This is unlikely to be a good fit if seniority took you away from the keyboard, or if you've never been responsible for anyone's work but your own.
• Product-minded. You start from what the business is trying to decide, not from what the pipeline does, and you can turn a vague stakeholder ask into a prioritized plan. This is unlikely to be a good fit if you need requirements handed to you, or if roadmap conversations feel like a distraction from the real work.
• 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.

This Role Is NOT

• A big-team leadership role. You start solo and then hire a Lead Analytics Engineer who reports to you; the team grows only as scope demands. If you want to direct a large org rather than build alongside a small one, this isn't it.
• A policy or committee job. There are no governance councils to chair and no binders to produce. 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. You build the platform and guardrails that let everyone else do that well.
• A finished system to babysit. Much of this doesn't exist yet. If you want to operate a mature data platform rather than build one, you'll be frustrated here.

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 (our internal AI toolkit), and any tool that makes you more effective or efficient
• Observability - an AI-powered Analytics Engineer agent (freshness monitoring, anomaly detection, dbt lineage) you'll scale up, plus the quality and impact tooling you'll add around it

You don't need every box checked. You need hands-on depth in the warehouse/pipeline layer and credible experience keeping a BI tool and tracking plan healthy at company scale.