GTM Engineer interview prep

Someone who turns the best manual work of a revenue team into systems. Give a crisp definition, then an example: "Our best SDR spent two hours a day researching accounts. I built a workflow that does the research for 500 accounts a day and writes a brief for each, so reps spend that time on calls."

What they look for: A definition tied to outcomes and a concrete example.

Cover the problem, the data sources, each step (enrichment, scoring, personalisation, routing), the tools, how you tested it, and the result in numbers: meetings booked, reply rate, hours saved. Mention what broke and how you fixed it.

What they look for: End-to-end ownership and a measured result.

Explain that you like being close to revenue and to the people using what you build, and that you enjoy fast feedback: a workflow shipped on Monday shows results by Friday.

What they look for: Genuine motivation for the hybrid role.

Name your strongest tools honestly. Then give a rule: use no-code tools for speed and handoff to non-engineers; write code when volume, cost, custom logic or reliability needs outgrow them, for example when processing 100,000 records or calling an API the tool does not support.

What they look for: Judgement about build versus configure.

Work backwards: 40 meetings at a 1% meeting rate needs about 4,000 well-targeted contacts a month. Define the ICP (company size, industry, tools such as their ERP, recent hiring or funding signals). Build the list, enrich with verified emails, score by fit and signal, write segment-specific messaging with an AI-personalised first line based on a real signal, and send through warmed domains with sensible daily limits. Track reply, positive reply and meeting rate by segment and kill weak segments weekly.

What they look for: Funnel math, signal-based targeting and deliverability.

Combine fit (company size, industry, role) with intent (pages viewed, product actions, pricing visits). Start with simple points agreed with sales, back-test against past deals to see which signals predicted closed-won, then adjust. Route high scores to reps within minutes and send low scores to nurture. Review monthly.

What they look for: Fit plus intent, validated against real outcomes.

Personalise on a real, recent signal (a hire, a launch, a job posting, a tool they use) rather than generic flattery. Use AI to draft the first line from that signal, keep the rest of the email human and short, and review a sample of each batch. Block outputs that mention nothing specific.

What they look for: Signal-based personalisation with quality control.

Check deliverability first: inbox placement, bounce rate, spam complaints, domain reputation and recent volume changes. Then targeting: did the list source or ICP change? Then messaging: did copy or the AI prompt change? Compare by domain, segment and sequence step to isolate the cause, fix it and add monitoring.

What they look for: Deliverability first, then targeting, then copy.

Route by territory (region, account size), then by account ownership so existing accounts go to their owner, then round-robin within the pod for new ones. Handle edge cases: duplicates, missing country, reps on leave. Set a speed-to-lead target and an alert when a lead is not touched in time.

What they look for: Clear rules plus edge cases and service levels.

Identify visiting companies, filter by ICP fit and high-intent pages such as pricing, find the right contacts, and alert the account owner with context, or trigger a short sequence for unowned accounts. Respect privacy rules in each region and avoid creepy messaging that mentions the visit directly.

What they look for: Intent use with good taste and compliance.

Join opportunities to activities and filter: SELECT o.account_id, o.id FROM opportunities o LEFT JOIN activities a ON a.opportunity_id = o.id AND a.created_at >= CURRENT_DATE - INTERVAL '14 days' WHERE o.status = 'open' GROUP BY o.account_id, o.id HAVING COUNT(a.id) = 0;

Then explain how you would turn this into a weekly alert to each owner.

What they look for: Correct join logic and a workflow use for the result.

Pick matching keys in order of reliability: email, then LinkedIn URL, then name plus company domain. Normalise them (lowercase, trim, strip tracking parameters). Decide which source wins for each field, merge, and log merges so they can be reversed. Prevent new duplicates with a check before records are created.

What they look for: Matching keys, field precedence and prevention.

Queue the records and process them in batches under the limit with backoff on errors; at 10 per second that is about 5.5 hours. Cache results so you never pay twice for the same record, skip records already enriched recently, and run it as a scheduled job with logging and a resume point.

What they look for: Throughput math, caching and resumability.

A webhook is an HTTP call one system sends to another when an event happens. Example: when a form is submitted, the form tool sends a webhook to your workflow, which enriches the lead, scores it and creates the CRM record with an owner within seconds.

What they look for: A clear definition with a GTM example.

Use separate sending domains from your main domain, set up SPF, DKIM and DMARC, warm new inboxes gradually, keep daily volume per inbox modest, verify emails before sending, remove bounces, make unsubscribing easy, and watch bounce and spam-complaint rates daily.

What they look for: Authentication, volume discipline and monitoring.

Inputs: account and contact names from the CRM meeting. Tools: company website, news search, the CRM history and product usage data. Output: a one-page brief with company context, likely pains, recent signals, past interactions and three suggested questions, each claim with a source. Check quality with reps on 30 briefs, measure usage and time saved, and limit cost per brief.

What they look for: Tool use, sourced output and adoption measurement.

Only let the model use facts you supply from enrichment, tell it not to invent anything else, ask it to return the source for each claim, validate outputs automatically (for example, block emails that mention a fact not in the input) and spot-check samples before each send.

What they look for: Grounding and automated checks.

Common uses: calling inbound leads within a minute to qualify and book meetings, reactivating old leads, confirming meetings to reduce no-shows, and letting reps practise calls with an AI roleplay. Measure connect rate, qualified meetings and cost per meeting against human calling, and make it easy to reach a person.

What they look for: Realistic use cases and comparison metrics.

Compare cost per record or per meeting (tool fees, AI tokens, data credits) with the value: extra meetings, pipeline and hours saved, versus the old process. Run it on a holdout group where possible so you measure real lift rather than activity.

What they look for: Unit economics and a fair comparison.

Ask what outcome it drives and compare it with what is in the backlog using expected pipeline impact and effort. Offer a quick version if one exists, agree on what moves down, and make the trade-off visible to the people affected.

What they look for: Prioritisation by impact, made transparent.

Talk to reps and watch them work. Usually the tool adds a step, lives in the wrong place or is not trusted. Fix the workflow (put it where they already work), show early wins from a few champions and remove the old manual path once it works.

What they look for: User empathy and adoption tactics.

Own it with numbers: what you expected, what happened, why (often targeting or deliverability), and what you changed. Show that you shut it down quickly rather than letting it run.

What they look for: Ownership and fast learning.

In pipeline and revenue terms: meetings, pipeline created, win rate and cost compared with the alternative, with a simple dashboard and one recommendation for what to do next.

What they look for: Business language, not tool language.

Mention specific habits: communities you are active in, tools you test in a sandbox, how you decide when a new tool is worth switching to (it must beat the current one on cost, quality or speed in a small test).

What they look for: A deliberate learning system.