Data Governance – Platform Manager

This is a fully remote position, open to applicants in Brazil.

📋 Description

• You will be the first individual at LawnStarter solely focused on data governance - responsible for ensuring the trustworthiness of our data.

• This encompasses the quality and timeliness of our source data, data pipelines, reports, the definitions of our metrics, the standards governing our Segment event tracking, the integrity of our Lightdash workspace, the data that powers our machine learning models, and the overall security of the data.

• This is a hands-on position. Initially, you will work independently, supported by the Analytics team but without direct reports - developing automation, conducting checks, rectifying issues, and establishing scalable processes. If the scope expands as anticipated, you will lay the groundwork for a team that you will build.

• Data quality and timeliness - implementing automated monitoring across source data, data pipelines, and reports; proactively identifying upstream schema and source changes before they disrupt downstream processes; resolving incidents as they arise.

• Data lineage and impact analysis - a dynamic map tracing from production source to warehouse model to dashboard, along with the associated processes: assessing the downstream impact on pipelines, metrics, and reports before any production changes are made, rather than discovering issues post-factum. The ultimate goal is to establish data contracts with engineering to catch breaking changes during their workflow, not ours.

• Lightdash - overseeing administration, workspace organization, permissions, and the overall rollout. Your responsibility is to provide the company with self-service autonomy while ensuring the workspace remains organized, allowing users to locate and trust the available data. Enablement is key - individuals should adhere to standards they have been trained on - and maintaining efficient queries and reasonable warehouse costs is essential.

• The semantic layer - we have recently launched it for our most critical metrics: a single governed definition per metric, in code. You will expand the definitions and mappings to encompass the rest while safeguarding the layer against unregulated growth as it scales.

• Event tracking governance - our governed Segment event catalog: evaluating new events against established standards, ensuring alignment with what production sends, and evolving the guardrails (naming conventions, property dictionaries, drift detection) as tracking evolves.

• AI data readiness - AI agents query our warehouse daily through Brain, our internal AI toolkit. You will oversee which data AI tools can access and maintain the warehouse's AI-legibility: ensuring it is documented, consistent, and safe for agents to query and retrieve accurate information.

• Data security and privacy - managing access controls, handling PII in compliance with US state privacy laws, and conducting regular reviews to determine who - and which AI tools - have access to specific data.

• The governance system itself - developing the documentation, ownership models, and review processes that keep all the above operations running smoothly without relying on heroics.

⛳️ Requirements

• Governance is your passion, not a task. You genuinely enjoy creating trustworthy and organized data systems - you’re the type who cannot ignore a flawed naming convention. This role may not suit you if you view governance merely as a stepping stone to "real" analytics work.

• AI-savvy. You utilize AI tools (Claude Code, Copilot, ChatGPT) daily to create quality checks, develop automation, address anomalies, and document your findings - efficiently covering ground that previously required a team. You also understand the reverse aspect: AI agents access our data daily, and ensuring the warehouse is safe and understandable for them is now part of governance. This may not be ideal for you if you are skeptical about AI tools or prefer manual methods.

• A proactive senior operator. You write SQL, debug the Airflow DAG, and configure permissions yourself - here, seniority signifies judgment and speed, not delegation. This may not be a good fit if your recent experience has been primarily in directing others and you would need a team to execute tasks.

• Automation-driven. Your instinct for any recurring check is to create a monitor rather than a checklist. This may not suit you if your quality practices rely heavily on manual reviews and discipline.

• An enforcer who is well-liked. You will hold engineers and analysts you do not manage to standards - achieving this requires clear rules, effective tools that facilitate compliance, and the courage to say no when necessary. This may not be a good fit if you avoid conflict or, conversely, if you enjoy being the "department of no."

🏝️ Benefits

• Base salary: $75k–$100k/year

• Equity: The entire company contributes to decisions based on the data you'll safeguard. As the trust in data increases, so does decision quality and overall company value. We want you to have a stake in that success.

• Fully remote: This role requires deep focus, whether you're building monitors or untangling pipelines, and we trust you to manage your work environment. Asynchronous collaboration is standard.

• Flexible PTO: We emphasize results. Take the time you need.