Data Governance & Platform Manager

What You'll Own

• Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen
• Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. The end-state is data contracts with engineering, so breaking changes get caught in their workflow, not ours
• Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. Enablement is part of the deal - people follow standards they've been taught - and so is keeping queries fast and warehouse costs sane
• The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales
• Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows
• AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer
• Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what
• The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics

Requirements

Who You Are

• Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy - you're the person who can't leave a broken naming convention alone. This is unlikely to be a good fit if you see governance as a stepping stone to "real" analytics work
• AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go - one person covering ground that used to take a team. You also see the reverse direction: AI agents consume our data daily, and making the warehouse safe and legible for them is part of governance now. This is unlikely to be a good fit if you're skeptical of AI tools or prefer to do everything manually
• A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself - seniority here means judgment and speed, not delegation. This is unlikely to be a good fit if your last few years were spent directing others and you'd need a team to execute
• Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. This is unlikely to be a good fit if your quality practice depends on manual review and discipline
• An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards - which takes clear rules, good tooling that makes compliance easy, and the spine to say no gracefully. This is unlikely to be a good fit if you avoid friction or, at the other extreme, enjoy being the department of no