Explain project scope, timeline, and delegation

# How to Craft and Deliver a Strong 10–15 Minute Project Deep Dive This guide gives you a step-by-step plan, a slide outline with timing, a worked example, and prepared answers for common follow-ups. ## 1) Pick and frame the right project Choose a project where you: - Owned or drove key decisions. - Can quantify impact with metrics. - Faced real constraints and trade-offs. - Coordinated across roles or teams. If bound by confidentiality, anonymize names and use relative improvements (for example, P95 latency decreased by about 40 percent) rather than sensitive figures. Narrative arc to follow: Context → Constraints → Plan → Execution → Results → Reflection. ## 2) Slide outline and time budget (10–15 minutes) - Slide 1. Title and one-line mission (0.5 min) - Slide 2. Problem and scope (1.0–1.5 min) - Slide 3. Success metrics and SLOs (1.0 min) - Slide 4. Architecture before (and after if space) (1.0–1.5 min) - Slide 5. Constraints and assumptions (1.0 min) - Slide 6. Estimation and timeline with milestones (1.5–2.0 min) - Slide 7. Risk management (1.0 min) - Slide 8. Delegation and coordination; manager vs IC collaboration (1.0–1.5 min) - Slide 9. Key technical and product trade-offs (1.0–1.5 min) - Slide 10. Cross-team communication (0.5–1.0 min) - Slide 11. Outcomes and validation (1.0 min) - Slide 12. What I would do differently (0.5–1.0 min) Buffer 1 minute for transitions and quick clarifications. ## 3) Worked example you can adapt: Reducing Transactions API latency Use this as a template. Replace with your details and numbers. ### Problem and scope - Problem: Transactions API had P95 latency around 520 ms during peak traffic, exceeding the P95 latency target of 350 ms and risking the 99.9 percent monthly SLO. - Scope: Reduce P95 latency to at most 300 ms without changing the API contract. Avoid database writes and breaking changes; no downtime. Preserve result correctness and pagination semantics. ### Success metrics and SLOs - Primary: P95 latency to at most 300 ms (target), P50 latency improved by at least 20 percent. - Reliability: Maintain 99.9 percent monthly SLO. Error budget at 99.9 percent for 30 days is approximately 43.2 minutes of allowable unavailability. - Secondary: 0 regression in correctness metrics and maintain cost neutrality or better. ### Architecture before - Request path: API → Gateway → Service → ORM → primary DB read → heavy join with transactions and merchants → JSON serialization → response. - Known hot path issues: large joins, uncached merchant enrichment, default page size 500, sparse indexing. ### Constraints and assumptions - No API contract changes; clients depend on existing pagination and fields. - Safe rollouts via feature flags and per-tenant toggles. - Compliance: No caching beyond 15 minutes for sensitive fields; encryption at rest preserved. - Operational: No read traffic moved to primaries; read replicas only. ### Estimation approach (PERT) and timeline Use PERT to de-risk estimates. - PERT expected duration for a task: E = (O + 4M + P) / 6 - Variance: Var = ((P − O) / 6)^2 Key tasks with example estimates: - T1: Add composite index for transactions by account_id, date - O 0.5 d, M 1 d, P 2 d → E = (0.5 + 4*1 + 2)/6 = 1.08 d - T2: Add read-through cache for merchant enrichment with 5 min TTL - O 2 d, M 3 d, P 5 d → E = (2 + 12 + 5)/6 = 3.17 d - T3: Introduce page size cap default 200 with override up to 500 - O 0.5 d, M 1 d, P 2 d → E = 1.08 d - T4: Query rewriting to remove N+1 joins, add covering index - O 2 d, M 4 d, P 7 d → E = (2 + 16 + 7)/6 = 4.17 d - T5: Tracing and baseline measurement, SLO dashboard - O 1 d, M 1.5 d, P 2.5 d → E = 1.67 d - T6: Canary rollout, load testing, and bake (per-tenant flags) - O 1 d, M 2 d, P 3 d → E = 2.0 d Sum of expected durations ≈ 13.17 d. Add 25 percent buffer for integration and unknowns ≈ 3.3 d. Planned schedule ≈ 16.5 working days (about 3.5 weeks) with milestones: - Week 1: T5 tracing and baseline, T1 index design and deploy. - Week 2: T4 query rewrite, initial correctness tests, T3 page-size cap. - Week 3: T2 cache layer, integration tests, docs. - Week 4: T6 canary rollout, load test, staged flags, post-change measurement. ### Risk management Sample risks and mitigations: - Cache staleness causing correctness regressions - Mitigation: Short TTL 5 min, background refresh, circuit breaker, per-tenant kill switch. - Hot key amplification and uneven cache distribution - Mitigation: Shard keys by account, include salt; monitor cache hit ratio and tail latency. - Index build impacts write performance - Mitigation: Concurrent index build during off-peak; monitor replication lag; rollback plan. - Page size default change surprises clients - Mitigation: Keep override up to 500; announce in changelog; A/B shadow traffic to measure impact. You can visualize with a simple 3x3 risk matrix and call out the top 3 by impact. ### Delegation and coordination - You: Tech lead; owned design doc, query rewrite, rollout plan, and metrics. - Backend IC: Implemented cache layer and TTL refresh worker. - SRE: Canary and observability; tuned dashboards and alerts. - QA or partner engineer: Correctness and regression testing with recorded traffic. Cadence: Daily standups, 2x weekly design office hours, weekly stakeholder sync. RACI: You accountable; others responsible for their tasks; product consulted on defaults; support informed for rollout and messaging. ### Manager vs IC collaboration - Manager: Alignment on scope, success metrics, and risk tolerance; secured time from SRE; unblocked cross-team dependencies. - IC peer collaboration: Paired on cache API and TTL strategy; ran small experiments to settle TTL and serialization format; conducted code reviews with shared ownership. ### Key technical and product trade-offs - Freshness vs latency via caching - With hit rate h and latencies L_cache and L_origin, average latency L_avg = h * L_cache + (1 − h) * L_origin. If h = 0.7, L_cache = 20 ms, L_origin = 520 ms, then L_avg ≈ 0.7*20 + 0.3*520 = 14 + 156 = 170 ms. - Trade-off: Too long TTL risks stale metadata; too short TTL reduces hit rate. - Page size default: Throughput vs client convenience - Lower default reduces per-request work and tail latency; clients can still request larger pages explicitly. - Query optimization vs infra scaling - Scaling replicas is faster but increases cost and may not fix tail latency; query rewrite and indexing gives sustainable wins. - Strict serialization vs faster formats - Chose safer but slightly slower JSON library to avoid edge-case regressions under time constraints. ### Cross-team communication - Stakeholders: Product, SRE, Support, Partner integrations. - Plan: Kickoff email with goals, risk register link, weekly updates; change log entry; rollout calendar; incident plan and contact rotation for canary period. ### Outcomes and validation - P95 latency reduced from ~520 ms to ~290 ms during peak (about 44 percent improvement). P50 from ~180 ms to ~110 ms. - SLO maintained; no correctness regressions in sampled comparisons of 50k responses. - Cache hit rate stabilized at ~72 percent with 5 min TTL; cost neutral due to fewer DB reads. - Rollout: 10 percent tenants day 1, 50 percent day 3, 100 percent by day 7 with no incidents. ### What I would do differently - Start with a joint design review earlier with SRE and partner teams to preempt risk on hot keys. - Build the canary-by-endpoint framework first; it would make future rollouts safer and faster. - Include automated load tests in CI to catch regressions pre-merge. ## 4) Prepared answers for common follow-ups - Handling scope creep - Use a change control and MoSCoW framework: Must have stays; Should have and Could have are deferred unless they protect the critical path. Any new scope must include quantified impact and trade-offs; update the plan and communicate. - Re-prioritization under deadlines - Re-slice scope to preserve outcome. Example: Ship page-size cap and index first to meet P95 target; postpone format swap. Communicate the impact on secondary goals and get alignment. - Conflict resolution - Seek data first: A 2-day experiment comparing TTL 2 min vs 5 min with error rates and hit ratio. If still inconclusive, use disagree and commit with time-boxed reevaluation. - Lessons learned - Measure twice and cut once: instrumentation early creates a shared source of truth. Pre-wire kill switches. Write a one-page decision log to avoid re-litigating choices. ## 5) Slide templates you can copy - Title: Reducing API P95 Latency by about 40 percent in 4 Weeks - Problem and scope: Current vs target, why it matters, scope boundaries. - Metrics and SLOs: P50 and P95 goals, error budget math, dashboards. - Before and after architecture: request path diagram; highlight hot spots. - Constraints and assumptions: contract, compliance, rollout safety. - Estimation and timeline: PERT table snippets; Gantt-style milestone bar. - Risks and mitigations: top 3 with owners. - Team and roles: RACI summary; meeting cadence. - Trade-offs: cache TTL, page size, query vs infra. - Cross-team comms: stakeholders, channels, schedule. - Outcomes: latency deltas, correctness, cost; screenshots of dashboards. - Retro: 2–3 improvements and why. ## 6) Guardrails and pitfalls - Do not overshare proprietary data; prefer relative improvements and anonymized diagrams. - Tie every decision to metrics and constraints; avoid hand-wavy claims. - Keep the story tight; 1–2 sentences per bullet. Practice to hit 12 minutes with 3 minutes buffer for questions. - Anticipate data freshness questions; have a clear answer on TTLs, invalidation, and fallbacks. Use this structure, swap in your project, and you will cover the exact dimensions interviewers probe for: problem framing, estimates, execution, risk, communication, decisions, and measurable impact.