/vpe-advisor

name: "vpe-advisor"
description: "Use when delivery is slowing, eng hiring is broken, team structure is unclear or you need to decide when to add an eng manager. Runs delivery_throughput_analyzer.py (DORA), eng_hiring_funnel_calculator.py and eng_team_structure_designer.py."
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: c-level
  domain: vp-engineering-leadership
  updated: 2026-05-13
  python-tools: delivery_throughput_analyzer.py, eng_hiring_funnel_calculator.py, eng_team_structure_designer.py
  frameworks: delivery-throughput, hiring-funnel, team-structure, production-discipline

VP of Engineering Advisor

Strategic engineering operations leadership for startup VPEs and founders without one. **Four decisions, no generic engineering survey:**

1. **Are we delivering at the right throughput?** — DORA 4 metrics + bottleneck identification (where work waits) 2. **How do we scale the eng hiring funnel?** — funnel math + pipeline gap + time-to-fill discipline 3. **What's our team structure — and when do we add a tech-lead manager?** — squad/tribe/chapter design + manager-trigger 4. **What's our production discipline?** — on-call rotation, deployment cadence, postmortem culture (reference-only)

This skill is **NOT a CTO skill**. CTO owns *what to build* (architecture, scaling cliffs, build-vs-buy). VPE owns *how to ship it reliably* (delivery, hiring, team structure, production operations). At early stage these are often the same person; at scale they're distinct roles.

This skill is **NOT a cs-engineering-lead replacement**. Engineering-lead owns day-to-day incident and on-call coordination. VPE owns the operating model that engineering-lead executes.

Keywords

VPE, VP of Engineering, VP Engineering, engineering operations, delivery throughput, DORA, deployment frequency, lead time for changes, mean time to recovery, MTTR, change failure rate, cycle time, lead time, throughput, engineering hiring, eng hiring funnel, technical interview, take-home, pair programming, hiring pipeline, time-to-fill, cost-per-hire, ramp time, engineering team structure, squad, tribe, chapter, Spotify model, conway's law, tech lead, engineering manager, EM, span of control, hiring funnel conversion, eng comp, leveling, IC track, manager track, deployment cadence, on-call rotation, postmortem culture, blameless retro

Quick Start

# Decision A: DORA 4 metrics + bottleneck identification
python scripts/delivery_throughput_analyzer.py                          # embedded sprint sample
python scripts/delivery_throughput_analyzer.py path/to/sprint_metrics.json

# Decision B: Hiring funnel health + pipeline gap
python scripts/eng_hiring_funnel_calculator.py                          # embedded 3-quarter sample
python scripts/eng_hiring_funnel_calculator.py path/to/funnel.json

# Decision C: Team structure recommendation + manager-trigger
python scripts/eng_team_structure_designer.py                           # embedded 25-engineer sample
python scripts/eng_team_structure_designer.py path/to/team.json

Key Questions (ask these first)

• **What's your cycle time, and where does the work spend most of its time waiting?** (If you don't know, you can't improve it.)
• **How long from commit to production?** (DORA "lead time for changes" — best predictor of overall team health.)
• **What's the escape rate?** (Bugs found in production vs caught in CI/staging. > 15% = quality discipline broken.)
• **When did the eng manager last write code?** (Manager-IC ratio is wrong if managers can't review code at all.)
• **What's the hiring funnel conversion at each stage?** (Source → screen → onsite → offer → accept. The leakage is the answer.)
• **What's the on-call rotation, and who's on it?** (If the same 3 people are always paged, the operating model is broken.)

Core Responsibilities

1. Delivery Throughput (DORA Metrics)

**The framework:** Google DORA's 4 key metrics (from "Accelerate", Forsgren/Humble/Kim 2018).

| Metric | What it measures | Elite | High | Medium | Low | |---|---|---|---|---|---| | **Deployment Frequency** | How often code reaches prod | Multiple/day | Daily-weekly | Weekly-monthly | < monthly | | **Lead Time for Changes** | Commit → production | < 1 hour | 1 day-1 week | 1 week-1 month | > 1 month | | **Mean Time to Recovery (MTTR)** | Incident detection → resolved | < 1 hour | < 1 day | 1-7 days | > 7 days | | **Change Failure Rate** | % of deploys causing incidents | 0-15% | 16-30% | 16-45% | 46-60% |

**Bottleneck identification — where does work wait?**

Cycle time = (PR creation → first review) + (review → approval) + (approval → merge) + (merge → deploy). The longest segment is the bottleneck.

Common bottlenecks:

• **PR review queue** (waiting for human reviewers) — fix: reviewer rotation + SLA
• **Test flakiness** (CI fails intermittently, re-runs needed) — fix: flaky-test budget + quarantine
• **Deploy gates** (manual approval, change-control board) — fix: progressive delivery + feature flags
• **Database migrations** (locking, scheduled windows) — fix: zero-downtime migration patterns

**Run** `delivery_throughput_analyzer.py` with sprint data to get DORA verdict + top bottleneck.

See `references/delivery_throughput.md` for the full DORA framework, anti-patterns, and what to fix first.

2. Engineering Hiring Funnel

**The trap:** "We can't find good engineers."

The reality: the funnel has 4-6 stages, each with a conversion rate. Find which stage is leakiest; fix that one. "Can't find good engineers" usually means top-of-funnel volume is too low or screening criteria are wrong.

**Standard funnel stages:**

| Stage | Healthy conversion | What it measures | |---|---|---| | Applied → Sourcer screen | 30-50% | Resume quality | | Sourcer → Recruiter screen | 50-70% | Basic fit | | Recruiter → Hiring manager | 60-80% | Team fit | | Hiring manager → Technical interview | 70-85% | Technical baseline | | Technical → Onsite (full loop)