Data Governance and Platform Manager

Location: Brazil, Rio de Janeiro, Rio de Janeiro, Brazil, Mexico, São Paulo, São Paulo, Brazil, Belo Horizonte, Minas Gerais, Brazil, Colombia

Department: Analytics

Workplace: remote

Employment Type: full

Description

About LawnStarter

LawnStarter is the nations leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. Were 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

Were 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 wont be stitching scattered sources together - the platform exists; your job is to make it trustworthy and keep it that way. Were mid-migration to Lightdash as our single BI platform, replacing Tableau and Metabase.

Heres 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 were hiring for.

The Role

Youll 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. Youll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing whats 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 youd build.

What makes this role different:

• Youre first. Governance has been everyones side job, so what exists today is yours to reshape - keep what works, redesign what doesnt, and your standards become the companys 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 Youll 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 whats there. Enablement is part of the deal - people follow standards theyve 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. Youll 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. Youll 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 Were replacing Tableau and Metabase with Lightdash. Done poorly, we trade two messy tools for one messy tool. Youll 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 both.

Finish and defend the semantic layer We just shipped our semantic layer for our most critical metrics - one governed definition per metric, so two dashboards, two numbers cant happen. The unglamorous truth: a long tail of metrics still needs definition and mapping, and a semantic layer only stays trustworthy if someone curbs its growth. Youll own both - extending coverage and keeping one-metric-one-definition true as the layer scales.

Tame event-tracking entropy Segment events power our funnels and product analytics, and theyre implemented by many engineers across many teams. The guardrails exist - a governed event catalog with naming standards, a property dictionary, a review lifecycle, and automated drift detection against production. Whats missing is a dedicated owner: someone who holds every new event to the standard, keeps the catalog matched to what production actually sends, and evolves the guardrails as tracking grows. Without that, entropy wins - events drift and silently degrade when features change.

Get ahead of breakage instead of chasing it Today, when production data changes upstream, we too often find out when a pipeline breaks or a stakeholder flags a wrong number. You wont start from zero - an AI-powered Analytics Engineer agent already runs freshness monitoring, metric anomaly detection, and dbt-based lineage checks - but it doesnt yet run at the scale or coverage we need. Youll take detection from partial to comprehensive, extend lineage beyond dbt (Segment events and Lightdash need stitching in), and wire it into engineerings change review, so a proposed production change comes with a downstream impact assessment instead of a postmortem. The end-state is data contracts: breaking changes caught in engineerings workflow, not ours.

What Success Looks Like (Year 1)

• Zero pipeline incidents from unannounced source-data changes - lineage and automation catch them before they break anything downstream, and production changes ship with an impact assessment instead of a postmortem.
• Zero freshness incidents - stakeholders never open a stale dashboard.
• Every area of the business manages on official, well-maintained metrics and dashboards - product, marketing, ops, and finance self-serve in Lightdash against a fully mapped semantic layer; Tableau and Metabase are retired; arguments about whose number is right dont happen. Not because you built the dashboards - because you built the system that keeps them trustworthy.
• Every Segment event has an owner and a standard - new events ship compliant, and degradation gets caught automatically, not by accident.
• Governance runs as a system - documented processes that would survive you taking a month off.

Requirements

Who You Are

• Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy - youre the person who cant 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 youre 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 youd 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. Youll hold engineers and analysts you dont 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 people-management role - yet. Youll work alone for a while. A team may grow under you if the scope demands it, but if you need direct reports on day one, this isnt it.
• A policy or committee job. There are no governance councils to chair and no binders to produce. When somethings broken, you fix it - with code, config, or a conversation.
• A BI analyst role. You wont 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 doesnt exist yet. If you want to operate a mature data platform rather than build one, youll be frustrated here.

Tech Youll Touch

• Warehouse & pipelines - Redshift, dbt, Airflow
• 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) youll scale up, plus the quality and impact tooling youll add around it

You dont 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.

Benefits

Compensation & Benefits

• Base salary: $75k–$120k/year
• 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.

LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment.