Data Governance & Platform Manager
LawnStarter · Curitiba, Paraná, Brazil
**About LawnStarter** LawnStarter is the nation's leading on\-demand marketplace for lawn care and outdoor services, with over $100M 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. 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. **What Makes This Role Different** * 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 * 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** * 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** **Make the Lightdash migration a step\-change, not a re\-platforming** We're replacing Tableau and Metabase with Lightdash. Done poorly, we trade two messy tools for one messy tool. You'll design the structure \- spaces, permissions, certification, naming \- that lets stakeholders self\-serve at the speed the company needs without creating an uncontrolled dashboard\-growth nightmare. The hard part: autonomy and tidiness pull in opposite directions, and you have to deliver bot