10 Instacart Growth Engineer (New Grad) Interview Questions (2026)

Outline

Pick a real example. Cover the hypothesis, the metric, the experimental design (A/B, before/after, observational), the result. Have specific numbers — sample size, effect size, p-value or confidence interval if you have them. Growth engineering is applied stats engineering; surface-level 'I A/B tested a button' answers lose.

Outline

Tie to the marketplace funnel (acquisition is expensive in this category; activation matters disproportionately), the data-rich product (every order is a labeled outcome), or the post-IPO context (public-company growth attention). Mention if you've thought about a specific Instacart growth lever. Generic 'I want to do growth' answers lose.

Outline

STAR. Growth engineers ship a lot of things that don't work — the bar is what you do next. Show the diagnosis (was it the targeting, the design, the metric definition?), the followups you tried, the lesson. Don't pretend everything you ship wins; that's a red flag.

Outline

STAR. Growth engineers often push for tests on assumptions that leadership treats as settled. Show how you made the case (small-but-credible signal, low-cost test, clear hypothesis). If you don't have a leadership-pushback story, frame a team-disagreement story with the same dynamics.

Outline

Input: per-user event list with timestamps. Output: count at each step, conversion percent vs the previous step. Algorithm: per user, walk events in order; record the maximum step reached (where event N comes after event N-1's time). Aggregate the max-step counts across users. Walk through edge cases: user skips a step, user repeats a step.

Outline

Per user, walk events in order; for each event named A, the next event becomes a transition. Aggregate transition counts across users. Return the most common. O(N) where N is total events. Walk through edge cases: A is the last event (no transition), repeated A (decide if each occurrence counts).

Outline

Was the sample size sufficient (powered for the expected effect)? What's the confidence interval, not just the point estimate? Are the variants balanced (random assignment held)? Are there novelty / day-of-week effects (the test may need a longer hold-out)? Are secondary metrics (revenue, retention) moving the same direction? Are there segments where the effect reverses (Simpson's paradox)? Walk through each.

Outline

Event schema: user_id, event_name, timestamp, properties. Pipeline: client SDK emits events → event queue → enrichment → durable store (data warehouse). For funnel queries: ordered event matching per user (event 1 happened before event 2 happened before event 3) within a time window. Discuss the difference between strict ordering and any-order, sessionization, and how you'd handle users who drop off and return.

Outline

Concept-level. Components: experiment config (treatment, control, traffic split, targeting rules), assignment service (deterministic hash of user_id + experiment to a bucket), exposure logging (record which user saw which variant), and analysis (computes lift, confidence intervals, segment breakdowns). Discuss why deterministic hashing matters (consistent assignment across sessions), how you'd ship a kill-switch, and what governance is needed (peer review of test plans).

Outline

Power = probability of detecting an effect if one truly exists. Standard target is 0.8 (20% chance of missing a real effect). Drivers: sample size, effect size, variance, significance level. Discuss why under-powered tests waste cycles (you can't conclude anything) and over-powered tests waste users (committing too many to a possibly-bad treatment). Mention sequential testing as an alternative for fast iteration.