Uber Behavioral Interview for Data Analyst (Mobility/Delivery/Freight) — Ownership, Customer Impact, and Marketplace EQ

This behavioral round probes how you think and operate as a Data Analyst within Uber’s high‑velocity, two‑sided marketplace. Expect 45–60 minutes of structured, story‑driven discussion focused on end‑to‑end ownership, customer obsession across multiple personas (riders, eaters, drivers/couriers, merchants), decision quality under ambiguity, and cross‑functional influence with PM, Eng, Ops, Policy, and Safety. What it covers and how it’s run: - Values in action: Examples where you acted like an owner, moved with urgency, and balanced long‑term platform health with near‑term metrics. Interviewers look for principled tradeoffs that protect safety and trust while driving growth. - Marketplace tradeoffs: Situations where you balanced supply/demand dynamics (e.g., pickup ETA vs. driver/courier earnings, fulfillment rate vs. cancellations, incentive spend vs. unit economics). Be ready to quantify impact with concrete metrics (trips, conversion, cancellation/defect rate, on‑time delivery, GBV, take rate, earnings/hour). - Ambiguity and scrappiness: Times you shipped insight or a decision framework despite messy telemetry, evolving schemas, or delayed events. Expect follow‑ups on how you validated data quality, handled late‑arriving data, or mitigated metric drift. - Experimentation ethos: How you framed hypotheses, aligned stakeholders on success metrics and guardrails, interpreted surprising A/B outcomes, and chose to roll back, iterate, or regionalize a launch. Ethical and safety considerations are probed explicitly. - Cross‑functional influence: Stories that show you unblocked decisions across regions and functions, navigated disagreement (e.g., Ops vs. Product goals), and communicated clearly to exec and non‑technical audiences. - Resilience and learning: A failure or incident (e.g., surge/incentive anomaly, quality regression) where you owned the recovery, documented learnings, and prevented recurrence via dashboards, alerts, or metric contracts. Format expectations: - 2–3 deep‑dives using STAR/SPA (Situation, Task, Action, Result; plus your Alternatives/Tradeoffs). Interviewers will press for specifics, counterfactuals, and numerics (baseline → lift, confidence, cost). - Signal areas: Ownership, Customer Focus across all sides of the marketplace, Data‑driven Judgment, Bias for Action with Safety, and Collaboration/Communication. - Uber‑specific nuance: Global‑local mindset (launching/guardrailing features by market), regulatory and safety sensitivity, and operational scale where small metric changes have large real‑world impact.