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Salary: USD 75,000 - 120,000 / annual
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $750M in annual bookings. The company is expanding beyond lawn care to become a one-stop shop for all home services across three brands (LawnStarter, Lawn Love, Home Gnome) on a shared platform.
You will be the first person at LawnStarter dedicated to data governance, owning whether the company's data can be trusted and building the roadmap to make it more trustworthy every quarter. The analytics team today consists of analysts who handle governance as side work; this role makes it the primary focus.
You'll own the entire data trust chain: source data quality and freshness, pipeline health, metric definitions, Segment event tracking standards, Lightdash workspace administration, semantic layer governance, data lineage and impact analysis, AI data readiness for the company's internal Brain toolkit, and data security and privacy controls. You'll discover what product, marketing, operations, and finance need from data, turn those needs into a prioritized roadmap, and sequence the work. You start solo, building automation, writing quality checks, fixing broken systems, and establishing processes that scale. Once established, you'll hire a Lead Analytics Engineer who reports to you, and the function grows from there.
Key problems you'll solve: turning business data needs into a roadmap that balances stakeholder requests against platform health; designing the Lightdash migration to enable self-service autonomy while maintaining workspace tidiness; extending and defending the semantic layer so one metric has one definition; taming event-tracking entropy by holding new events to standards and keeping the catalog matched to production; and getting ahead of breakage by extending lineage monitoring and impact analysis so production changes ship with downstream assessments instead of postmortems.
Year 1 success means zero pipeline incidents from unannounced source changes, zero freshness incidents, every business area managing on official well-maintained metrics in Lightdash, every Segment event with an owner and standard, and governance running as a documented system.
This is a hands-on role. You'll write SQL, debug Airflow DAGs, configure permissions, and build alongside the analytics team. You've managed people before, allocating their time and owning their priorities, and you never stopped building yourself. You use AI tools daily (Claude Code, Copilot, ChatGPT) to build quality checks, write automation, and document. You're product-minded, starting from what the business is trying to decide, and you can turn vague asks into prioritized plans. You're automation-first, building monitors instead of checklists. You hold people to standards gracefully, with clear rules and good tooling that makes compliance easy.
Tech stack: Redshift, dbt, Airflow, Fivetran, Segment, Lightdash (primary), Tableau and Metabase (sunsetting), Claude Code, and an AI-powered Analytics Engineer agent you'll scale up.
This role is not a big-team leadership position, a policy or committee job, a BI analyst role, or a finished system to babysit. You'll build alongside a small team, not direct a large org.
REQUIREMENTS:
- Hands-on depth in the warehouse/pipeline layer (Redshift, dbt, Airflow experience expected)
- Credible experience keeping a BI tool and tracking plan healthy at company scale
- Proven track record managing people and allocating their time
- Daily use of AI tools (Claude Code, Copilot, ChatGPT) for building quality checks, automation, and documentation
- Product-minded approach: able to translate business needs into prioritized data roadmaps
- Automation-first mindset: preference for building monitors over manual processes
- Ability to enforce standards gracefully across teams you don't directly manage
- Genuine passion for data governance and system trustworthiness (not a stepping stone to other work)
