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 $100M in annual bookings. The company is expanding beyond lawn care to become a 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 The analytics team supports the entire company including product, marketing, operations, and finance. The data platform includes a centralized Redshift data warehouse, modeled in dbt and orchestrated by Airflow, with Segment feeding event data. The team is mid-migration to Lightdash as the single BI platform, replacing Tableau and Metabase. Currently, data quality, tracking standards, and platform hygiene are side tasks handled by analysts. The Role The Analytics Engineering Manager will be the first dedicated person for data governance, responsible for data trustworthiness and the roadmap to improve it quarterly. Responsibilities include ensuring quality and freshness of source data, pipelines, reports, metric definitions, Segment event tracking standards, Lightdash workspace health, data feeding ML models, and data security. The role involves collaborating with product, marketing, ops, and finance to prioritize platform work. It is a hands-on role starting solo, building automation, checks, fixing issues, and establishing scalable processes. Eventually, the manager will hire a Lead Analytics Engineer and grow the function as needed. Key differentiators of the role: - First dedicated governance role, reshaping existing standards. - Owns the data roadmap, discovering business needs and prioritizing work. - Whole-stack ownership from source data to ML models. - Leading the migration to Lightdash, setting up permissions and norms. Responsibilities: - Develop and manage the data roadmap balancing business needs and platform health. - Automate monitoring of data quality and freshness, resolve incidents. - Maintain data lineage and impact analysis to assess downstream effects of changes. - Administer Lightdash workspace, permissions, and rollout. - Extend and guard the semantic layer with governed metric definitions. - Govern Segment event tracking catalog, standards, and drift detection. - Ensure AI data readiness for internal AI tools querying the warehouse. - Manage data security, privacy, access controls, and compliance with US state laws. - Maintain governance documentation, ownership models, and review processes. Challenges to solve: - Convert business requests into a prioritized data roadmap. - Ensure Lightdash migration improves platform usability without dashboard sprawl. - Complete and maintain a trustworthy semantic layer. - Manage event-tracking standards and prevent entropy. - Proactively detect and prevent data pipeline breakages with automation and lineage. Success metrics for year 1: - Zero pipeline incidents from unannounced data changes. - Zero freshness incidents; stakeholders never see stale dashboards. - All business areas use official, well-maintained metrics and dashboards. - Every Segment event has an owner and meets standards. - Governance operates as a documented, sustainable system. Requirements: - Passion for data governance as a craft. - Daily use of AI tools for automation, anomaly triage, and documentation. - Hands-on manager who codes, debugs, and configures personally. - Product-minded, able to translate vague requests into prioritized plans. - Automation-first approach to quality checks. - Ability to enforce standards with clear rules and good tooling. Role exclusions: - Not a large-team leadership role; starts solo with potential to hire one lead. - Not a policy or committee role; hands-on fixing with code and conversations. - Not a BI analyst role; focuses on platform and guardrails, not dashboard building. - Not a mature system babysitting role; involves building and evolving the platform. Technologies involved: - Warehouse & pipelines: Redshift, dbt, Airflow - Ingestion: Fivetran, custom Airflow pipelines - Event tracking: Segment - BI tools: Lightdash (primary), Tableau and Metabase (sunsetting) - AI tooling: Claude Code, Codex, Brain (internal AI toolkit) - Observability: AI-powered Analytics Engineer agent for monitoring and anomaly detection Benefits: - Base salary $75k-$120k/year - Fully remote with asynchronous collaboration - Flexible PTO Equal employment opportunity statement included.