Job Title: Analytics Engineering Manager, Data Platform & Governance
Company: LawnStarter
Job Type: Fully remote; async collaboration is the norm
Location: Remote.
Salary/Pay: USD 75,000 - 120,000 per year (base salary). Benefits include flexible PTO.
Experience: Hands-on depth in the warehouse and pipeline layer, credible experience keeping a BI tool and tracking plan healthy at company scale, and prior accountability for other people's output. Years of experience aren't stated.
Skills Required:
- Hands-on depth in data warehousing and pipelines (Redshift, dbt, Airflow)
- Experience keeping a BI tool (Lightdash, Tableau, or Metabase) and an event-tracking plan (Segment) healthy at company scale
- Daily use of AI tools (Claude Code, Copilot, ChatGPT) to build quality checks, write automation and triage anomalies
- Automation-first approach to data quality monitoring
- Product-minded: able to turn vague stakeholder asks into a prioritised roadmap
- Hands-on management experience, writing SQL, debugging DAGs and configuring permissions personally
- Ability to hold engineers and analysts to standards, saying no gracefully
- Knowledge of data security, PII handling, and privacy requirements
Application Deadline: Oct 31th, 2026
Description:
LawnStarter is a leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings, operating three brands on one shared platform. It is hiring its first dedicated owner of data governance, responsible for whether the company's data can be trusted and for the roadmap that improves it each quarter. The role starts solo, working alongside a small senior analytics team, and later opens a Lead Analytics Engineer role reporting to you. It is hands-on, not a policy or committee job.
Key responsibilities include:
- Owning the data roadmap by working with product, marketing, ops, and finance to discover needs and prioritise platform work
- Data quality and freshness: automated monitoring across source data, pipelines and reports, and running incidents to resolution
- Data lineage and impact analysis, working toward data contracts with engineering so breaking changes are caught in their workflow
- Administering the Lightdash rollout (structure, permissions, enablement), as the company replaces Tableau and Metabase
- Extending and protecting the semantic layer so each metric has one governed definition
- Governing the Segment event catalog (naming, property dictionary, drift detection)
- Making the warehouse safe and legible for AI agents that query it daily
- Data security and privacy: access controls, PII handling and retention under US state privacy laws
- Building the documentation and review processes that keep governance running without heroics
Year 1 success looks like zero pipeline and freshness incidents from unannounced changes, every area of the business using official metrics in Lightdash with Tableau and Metabase retired, every Segment event having an owner and a standard, and governance running as a documented system.
How to Apply:
Apply through We Work Remotely.