went through okta's DS interview process for a mid-level role on their customer analytics team. sharing this because there wasn't much online about the DS-specific loop and i had to piece it together from scattered sources.
round structure: recruiter screen (30 min) hiring manager screen (45 min) SQL/data skills round (60 min) case/product analytics round (60 min) stats and probability round (45 min) behavioral (45 min)
SQL round: this was harder than i expected. they use a tool like CoderPad with a mock schema (user activity table, events table, something like a product log). questions i got: find users who performed action A but not action B within 7 days of signup (classic funnel, window functions) calculate 30-day rolling retention by cohort write a query to flag anomalous sessions based on session length percentile
if you're rusty on window functions (RANK, ROW_NUMBER, LAG, LEAD), practice those specifically. CTEs are expected. performance considerations came up: 'how would you optimize this if the events table had 10B rows'.
case/product analytics round: something like: okta wants to understand why enterprise customers are not adopting MFA for all their users. how would you approach this? they want: hypothesis generation, metric selection, analysis plan, and then 'what would you actually do with the findings'.
stats round: explain p-values to a non-technical stakeholder how would you design an experiment to test a new onboarding flow when you can only run it on 20% of new customers describe a situation where a model you built was technically correct but the business acted on it wrong
the last one is a great question and worth having a real answer for.
overall: okta's DS bar feels like a serious mid-level analytics bar, not research-heavy. less ML theory than i expected, more applied SQL and business case judgment. the 'why does this metric move' type thinking matters more than knowing SVMs cold.