Data Governance & Platform Manager

FyrFly Venture Partners; Brasil; Presencial; BRL 764 916 - 1 070 882; Tempo integral

Resumo da oferta

LawnStarter is expanding its Analytics function and searching for a Data Governance Lead who will own data quality, lineage, and trust across source data, pipelines, and reports.

You will shape the Lightdash implementation, establish data contracts, and help ensure privacy and security while enabling self-serve analytics across the organization.

Qualificações

• First dedicated data governance role.

Responsabilidades

• Own data governance from source to dashboards and ML models.

Conhecimentos

Data governance

Data quality

Data lineage

Lightdash

Event tracking

Data contracts

Security & privacy

dbt

Airflow

Redshift

Segment

Ferramentas

Lightdash

dbt

Airflow

Redshift

Segment

Descrição da oferta de emprego

Overview

LawnStarter is the nation\'s leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. We are 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.

Analytics at LawnStarter supports the entire company—product, marketing, operations, and finance—driven by data. The foundation is a centralized Redshift data warehouse with data 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. Today, data quality, tracking standards, and platform hygiene are handled as side work by analysts; this role exists to make them a core, scalable function.

The Role

You will be the first person at LawnStarter dedicated to data governance—the owner of whether our data can be trusted. Responsibilities include ensuring the quality and freshness of source data, pipelines, and reports; defining metrics; upholding standards for Segment event tracking; maintaining the Lightdash workspace; supporting data used in machine learning models; and ensuring data security. This is a hands-on role performed initially solo, with the Analytics team around you, building automation, writing checks, fixing issues, and establishing scalable processes. If the scope grows, you may build a team.

What makes this role different: you are first. Governance has been everyone\'s side job, so you will reshape existing practices, retain what works, redesign what doesn\'t, and set standards for the company. You will have end-to-end ownership from source data to dashboards and ML models, ensuring trust across the entire data chain. You will help shape the Lightdash implementation, including permissions, structure, and norms, to enable self-serve while maintaining organization and trust. The role involves working through a live migration to Lightdash and establishing governance early to prevent bad habits later.

What You'll Own

• Data quality and freshness: automated monitoring across source data, pipelines, and reports; detect upstream schema or source changes before they affect downstream systems; run incident response when issues occur.
• Data lineage and impact analysis: maintain a living map from production sources to the warehouse, models, and dashboards; assess downstream impact of production changes before deployment; establish data contracts with engineering.
• Lightdash administration: managing workspace structure, permissions, and rollout; enable self-serve while keeping the workspace tidy and trustworthy; ensure fast queries and controlled costs.
• Semantic layer governance: extend governed metric definitions in code and guard against uncontrolled growth as the system scales.
• Event tracking governance: curate the Segment event catalog, align new events with standards, ensure alignment with production data, and evolve naming, property dictionaries, and drift detection.
• AI data readiness: govern data access for AI tools, keeping the warehouse machine-learning friendly and safely queryable.
• Data security and privacy: implement access controls, manage PII handling and retention under US state privacy laws, and periodically review who and which AI tools can access data.
• Governance framework: maintain documentation, ownership models, and review loops that keep governance running smoothly.

Problems to Solve

• Make the Lightdash migration a step-change, not a straight re-platforming: design the structure (spaces, permissions, naming) to enable fast, self-serve use without uncontrolled dashboard growth.
• Balance autonomy and tidiness: deliver both speed for stakeholders and governance controls to prevent chaos in dashboard growth.
• Finish and defend the semantic layer: extend governance to remaining metrics and ensure consistency across the organization.