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 $150M 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 is small and senior, supporting the entire company including product, marketing, operations, and finance. The data platform is built on 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, governance tasks are side work done by analysts, and this role is dedicated to data governance. The Role: You will be the first dedicated person at LawnStarter responsible for data governance, owning the trustworthiness of data 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 machine learning models, and data security. You will collaborate with product, marketing, operations, and finance to understand business data needs and prioritize platform work. This is a hands-on managerial role starting solo, building automation, fixing issues, and establishing scalable processes. Eventually, you will hire and lead a Lead Analytics Engineer. Key Differentiators: - First dedicated governance role, reshaping existing governance standards. - Own the data roadmap, discovering and prioritizing business needs. - Full ownership from source data to ML models. - Lead the migration to Lightdash, setting up permissions and structure. Responsibilities: - Develop and manage the data roadmap balancing stakeholder needs and platform health. - Implement automated monitoring for data quality and freshness. - Maintain data lineage and impact analysis to assess downstream effects of changes. - Administer Lightdash workspace, permissions, and rollout. - Extend and guard the semantic layer ensuring one governed definition per metric. - Govern Segment event tracking catalog, standards, and drift detection. - Ensure AI data readiness for internal AI tools querying the warehouse. - Manage data security and privacy including access controls and compliance with US state privacy 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 and governance. - Complete and maintain the semantic layer. - Prevent entropy in event tracking and maintain standards. - Proactively detect and resolve data pipeline breakages. Success Metrics (Year 1): - Zero pipeline incidents from unannounced source data changes. - Zero freshness incidents with stakeholders always seeing up-to-date dashboards. - All business areas self-serve on official, well-maintained metrics and dashboards. - Retire Tableau and Metabase. - Every Segment event has an owner and meets standards. - Governance operates as a documented, sustainable system. Requirements: - Passion for data governance and making data systems trustworthy. - Daily use of AI tools (Claude Code, Copilot, ChatGPT) for automation and anomaly triage. - Hands-on manager accountable for others' output and still actively building. - 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 is not: - Large team leadership initially; starts solo with potential to grow. - Policy or committee role; hands-on fixing with code, config, or conversation. - BI analyst role focused on dashboard building. - Babysitting a finished system; role involves building and maturing the platform. Tech Stack: - Warehouse & pipelines: Redshift, dbt, Airflow - Ingestion: Fivetran, custom Airflow pipelines - Event tracking: Segment - BI: Lightdash (primary), Tableau and Metabase (sunsetting) - AI tooling: Claude Code, Codex, Brain (internal AI toolkit) - Observability: AI-powered Analytics Engineer agent for freshness monitoring, anomaly detection, dbt lineage Benefits: - Base salary $75k-$120k/year - Fully remote work with deep focus environment - Async collaboration - Flexible PTO - Equal employment opportunity and compliance with nondiscrimination laws