Data Analytics Operator

DataDrone Inc.; Cheyenne, Northern (WY, KY); Hybride; USD 90 000 - 130 000; Plein temps

Résumé du poste

DataDrone Inc. in Cheyenne, WY, seeks a data analytics lead to own the data truth layer—event tracking, behavioral signals, health scores, dashboards, and analytics reporting.

You will validate events, build and tune signal logic, and calibrate health scores. You’ll translate complex metrics into actionable insights for the team.

The role emphasizes documentation, proactive analysis, and dashboards that drive decisions, with a focus on data accuracy and a cadence of weekly insights.

Qualifications

• Experience with event-based analytics platforms or equivalent.
• Ability to write SQL queries to validate data.
• Experience building dashboards used for business decisions.
• Ability to explain metrics to non-technical stakeholders.
• Strong data validation mindset.

Responsabilités

• Define and validate event tracking — what gets measured; ensure correctness.
• Build and maintain signal logic — behavioral patterns that trigger actions.
• 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 playbooks.
• Maintain health score model documentation in playbooks.

Connaissances

Event-based analytics

SQL querying

Dashboarding

Data validation

Communication of metrics

Outils

CDP

Dashboarding tools

Description du poste

DataDrone 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.

What You’ll Do

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 Rhythm (How You Operate)

• 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.

Key Responsibilities

• 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

What We’re Looking For

• 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.

Bonus Points For

• 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

The Mindset We Hire For

• 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.

Success in This Role

• 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.

Tools You’ll Use

Analytics platform, web analytics, SQL, dashboarding tools, Slack, Google Drive, AI copilot.

Why Join Us

You won’t build dashboards nobody looks at. You won’t track metrics that don’t matter. You’ll own the data layer that every decision in the company depends on. When the health score flags a risk, the success team acts. When the dashboard shows a trend, the founding team adjusts strategy. Your accuracy is the foundation.

5 hours of focused analytics work per day. Precision over volume.

Self-Check Before You Apply

• When was the last time you caught a data accuracy issue before anyone else noticed? (If you can’t remember, your validation process might be too loose.)
• Can you explain a health score model to a non-technical operator and have them understand when and why to act on it?
• Do you validate event data regularly — or trust that it’s still correct because it was correct at setup?
• When someone asks "why did this metric change?" can you trace it to a root cause within an hour?
• Are your dashboards used for decisions — or are they status screens that people glance at and forget?
• Is every metric definition, signal threshold, and calculation formula you maintain documented outside your head?

If three or more are yes, that’s you — apply.