Data Analytics Operator

Data Drone builds 0-1 SaaS products and the team that ships them. No bosses, no managers — the playbook manages, the copilot tracks. Every role owns a small, defined space with extreme accountability. Behind every decision we make is data that someone validated, scored, and surfaced. That someone is this role.

You’ll own the data truth layer. Event tracking, behavioral signals, health scores, dashboards, and analytics reporting. When the team makes a decision, your data made it possible. When a churn signal fires and the success team intervenes, your signal logic detected the risk. When the founding team looks at a dashboard, your accuracy determines whether they see reality or fiction.

• Daily: validate event data and watch for anomalies — events correct at launch can break silently.
• Weekly: deliver an analytics insight proactively — an intelligence briefing, not a status update.
• Per signal: configure and verify behavioral signal logic — if [condition] for [duration] then [trigger].
• Monthly: calibrate the health score model against actual outcomes; retire dashboards nobody uses.
• Always: document event schemas, signal thresholds, and health-score formulas outside your head.

• Define and validate event tracking — what gets measured, whether it's measuring correctly
• Build and maintain signal logic — behavioral patterns that trigger interventions
• Own the health score model — dimensions, weights, thresholds, calibration
• Build and maintain dashboards — accurate, current, actionable
• Deliver weekly analytics insights proactively — not just when asked
• Maintain event/signal documentation in the CDP Onboarding Playbook
• Maintain health score model documentation in the Client Success Playbook

• You’ve worked with event-based analytics (CDP, product analytics, or behavioral tracking platforms).
• You can write SQL queries to validate data, investigate anomalies, and answer business questions.
• You’ve built dashboards that people actually use for decisions — not decoration.
• You understand signal logic: if [condition] for [duration] then [trigger]. You’ve configured or designed this.
• You can explain a metric to a non-technical stakeholder and have them understand what it means and why it matters.
• You validate data accuracy obsessively. "It looks right" is not validation.

• Analytics-platform experience
• Web / app analytics experience
• Experience building health score models for SaaS customer bases
• Experience with churn prediction or customer behavior modeling
• Experience writing event schemas and signal definitions

• Data accuracy is non-negotiable. Bad data is worse than no data. You’d rather have a gap than a lie.
• Dashboards serve decisions. If nobody uses a dashboard, retire it. If everyone uses it, make sure it’s right.
• Signals must be trusted. A false alarm wastes the success team’s time. A missed signal costs an account. Both are your failures.
• Proactive over reactive. You share insights before people ask. The weekly debrief isn’t a status update — it’s an intelligence briefing.
• Document everything. Event schemas, signal thresholds, health score formulas — if it’s in your head, it’s fragile.
• Validate, don’t assume. Events that were correct at launch can break silently. You check. Every week.

• 30 Days — Full event audit completed. Dashboards reviewed. Health score model understood. Client Success Operator relationship established.
• 60 Days — Event coverage at 100%. Signal accuracy tracked. Dashboards current and actionable. Weekly insights flowing proactively.
• 90 Days — Health score validated against outcomes. Signals trusted. Dashboards drive decisions. Documentation handoff-ready.