Overview
You'll work in Okta's Growth Lab, a team focused on building scalable GTM infrastructure to drive growth across Marketing and Sales. You're optimizing how a 7K+ person public company generates and converts pipeline—running experiments, analyzing data, and turning insights into repeatable systems. This sits at the intersection of marketing ops, rev ops, and growth strategy.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Growth Marketing/Rev Ops hybrid |
| Sales Motion | Supporting both inbound marketing and sales outreach motions |
| Deal Complexity | N/A - infrastructure role |
| Sales Cycle | N/A - you're optimizing the funnel, not closing deals |
| Deal Size | N/A |
| Quota (est.) | No sales quota - measured on pipeline influence, conversion rates, experiment velocity |
Company Context
Stage: Public (traded as OKTA)
Size: 7,258 employees
Growth: Building out a "Growth Lab" function suggests they're investing in more product-led/scalable motions beyond their traditional enterprise sales focus
Market Position: Category leader in identity/access management, competing against Microsoft, Ping Identity, and others. Mature product with strong brand recognition.
GTM Reality
Current State:
- Historically enterprise-focused with long sales cycles and relationship-driven deals
- Now building infrastructure for "scalable growth" - likely means testing mid-market motions, PLG elements, or more efficient enterprise pipeline generation
- Large sales org that needs repeatable systems, not one-off campaigns
Your Role in the Machine:
- You're the person figuring out what growth experiments to run and how to scale what works
- Bridge between marketing (campaigns, content, demand gen) and sales (pipeline quality, conversion rates)
- Report findings and recommendations up, but also need to get buy-in from field teams to actually implement changes
What You'll Actually Do
Time Breakdown
Data Analysis (30%) | Experiment Design (25%) | Cross-functional Meetings (25%) | Documentation/Reporting (20%)
Key Activities
- Pipeline Analysis: Pull data from Salesforce, marketing automation, and product usage to understand where leads come from, where they convert, and where they drop off. Build dashboards and reports that tell the story.
- Growth Experiments: Design A/B tests on landing pages, email sequences, trial-to-paid conversion flows, or sales outreach cadences. Work with marketing ops, web teams, and sales to execute. Many experiments will fail or show marginal results.
- Cross-functional Alignment: Spend a lot of time in meetings explaining why you want to test something, getting buy-in from sales leadership or marketing directors, and navigating internal politics about who owns what metric.
- Operationalizing Wins: When something works, document it, build the playbook, and work with enablement/ops teams to roll it out. This is harder than finding the insight—requires change management in a large org.
The Honest Reality
What's Hard
- Proving Impact: Attribution is messy. You'll constantly be defending whether your experiments actually moved the needle or if it was just timing/seasonality.
- Internal Politics: Everyone has opinions on growth strategy. Marketing thinks they own it, Sales thinks they know best, Product wants more trials. You're stuck coordinating.
- Slow Execution: Running tests at a public company with enterprise customers means legal reviews, brand guidelines, and 6 weeks to change a landing page headline.
- Most Experiments Fail: You'll run 10 tests and maybe 2 will show meaningful results. That's the job, but it can feel like spinning your wheels.
What Success Looks Like
- You identify 2-3 scalable levers that actually increase pipeline or conversion rates by meaningful amounts (10%+, not 2%)
- Sales and marketing teams are actually using systems/playbooks you built
- Leadership references your analysis when making GTM investment decisions
Who You're Working With
Internal Stakeholders:
- Growth Lab leader (Rachael T.)
- Marketing operations, demand gen, content teams
- Sales ops, sales leadership, enablement
- Product/analytics teams for usage data
What They Care About:
- Sales: Does this actually create pipeline they can work? Is lead quality improving or just volume?
- Marketing: Are we getting credit for our contribution? Does this make our campaigns look good?
- Executives: What's the ROI? Can this scale? How fast can we roll it out?
Requirements
- Experience running growth experiments (A/B tests, funnel optimization, conversion rate work)
- Comfort with data analysis - SQL, Excel/Sheets, Tableau/Looker or similar
- Understanding of B2B SaaS sales cycles and marketing funnels
- Ability to influence without authority - you'll be coordinating across teams that don't report to you
- Track record of turning insights into implemented systems, not just PowerPoint recommendations
- Scrappy mindset - "growth hacker" suggests they want someone who can move fast and try things, not just analyze