Prioritize competing engineering requests

## What a strong answer demonstrates - You prioritize by **business impact, urgency, risk, and effort**, not by who asks loudest. - You create a **transparent intake + prioritization framework** and socialize it. - You manage stakeholders proactively and de-risk delivery. ## Step-by-step approach ### 1) Clarify each request (don’t accept vague asks) For each team, quickly standardize: - **Goal:** what decision will this analysis/experiment enable? - **Success metric:** what metric moves, by how much, by when? - **Time sensitivity:** hard deadlines (launch, exec review), or “nice to have”. - **Effort & dependencies:** data availability, instrumentation needs, eng work, approvals. - **Risk:** potential revenue/user harm, legal/privacy risk, correctness risk. ### 2) Use a scoring rubric (lightweight, repeatable) Example rubric (score 1–5): - **Impact** (revenue, retention, advertiser outcomes) - **Confidence/clarity** (is the problem well-defined?) - **Urgency** (launch gating vs exploratory) - **Effort** (lower effort = higher score) - **Risk reduction** (does it prevent major harm?) You can turn this into a simple priority score like: \[ \text{Priority} = (Impact + Urgency + RiskReduction + Clarity) - Effort \] ### 3) Offer options, not just “yes/no” When two requests compete, propose: - **Option A:** Do request 1 fully now; request 2 next sprint. - **Option B:** Do a **thin-slice** for request 2 (minimum viable analysis) now + deeper follow-up. - **Option C:** Parallelize by shifting work: ask for **eng instrumentation** first, while you analyze existing data. ### 4) Make tradeoffs explicit (and document) Communicate: - What you will deliver (scope) - By when - What you are not doing (de-scoped items) - Risks/assumptions A short written doc (1 pager) prevents re-litigation later. ### 5) Handle escalation / senior pressure - Ask for the **decision criteria**: “If we override, which goal are we optimizing—revenue this quarter, launch readiness, or platform risk?” - Provide the **opportunity cost**: “If we do X, Y slips by 2 weeks; expected impact is …” - If leadership insists, align, execute, and **record the decision + rationale**. ## Common pitfalls - Agreeing before clarifying success metrics. - Prioritizing by relationship rather than impact. - Not de-risking via instrumentation checks and data quality. - Not setting expectations on iteration vs one-shot perfection.