Why this role exists
neatlogs helps teams understand why their AI agents fail in production, fix the cause, and check whether the fix worked. A team can hear about us, sign up, and still never connect a trace or reach the point where an investigation becomes useful. Another may be ready to talk to sales, but the signal gets lost between the product and a spreadsheet.
We’re building content and sales alongside a live product. We need someone to connect those efforts to what people actually do: instrument the journey, build useful automations, find where teams get stuck, and improve the experience from first visit through repeated use. You’ll work in code, data, and GTM tools, and own the results of what you build.
What you’ll work on
- Product and funnel analytics. Define a small, trustworthy set of events and stages: visit, signup, workspace setup, first trace, first useful investigation, return usage, and expansion. Build dashboards that show where teams move, stall, or drop off. Check the underlying data before making a recommendation from a chart.
- Lifecycle communications. Build and test onboarding, activation, and re-engagement journeys across email and appropriate in-product surfaces. Use real product behavior to decide when a message is useful, what it should help someone do, and when it should stop. Work with marketing and product on the words and experience.
- GTM automations. Connect forms, product events, CRM records, and communication tools. Automate routing, enrichment, follow-up triggers, and handoffs where they save time or prevent a good opportunity from being missed. Keep humans in the loop for judgment-heavy steps.
- Experiments that change the funnel. Find a specific point of friction, form a hypothesis, ship a small change, and measure whether it helped the intended team. That might be a signup step, integration prompt, lifecycle message, or sales handoff. Record what changed so we don’t mistake correlation for a win.
- A usable data foundation. Make identities, accounts, events, and consent states consistent enough to support analysis and communications. Notice duplicate users, missing events, broken syncs, and misleading attribution before they become the team’s operating picture.
Your first few months
- Month one: Map how a team currently moves from discovering neatlogs to getting value from a real agent run. Audit the events and GTM tools we already have, speak with sales, marketing, and product, and fix one obvious break in the journey or its measurement.
- Months two and three: Establish a funnel view the team trusts and ship a small number of behavior-based lifecycle journeys or routing automations. Run a focused experiment on a meaningful activation or handoff problem and show what happened.
- After that: Own the instrumentation and automation roadmap for the customer journey. Keep improving the path to first value and repeat use, while helping sales and marketing act on reliable signals.
What good work looks like
Signups are up, but few new teams reach a useful investigation. You verify that the events mean what everyone thinks they mean, look at the path by account and use case, and talk to people who got stuck. You find that a particular integration step is unclear. You improve the prompt with product, send a timely message only to teams for whom it is relevant, and measure whether more of them connect a real trace and come back.
You can build the automation yourself, explain its failure modes, and stop or change it when it creates noise. A working workflow is not a success if it makes the experience worse or hands sales a list of people who are not ready to talk.
What we’re looking for
- You’ve built and operated GTM or product growth systems yourself. Show us an automation, data pipeline, funnel analysis, or lifecycle journey you personally implemented and improved.
- You can work with APIs, webhooks, SQL, and scripts, and you know when a no-code tool is enough and when a more durable solution needs code.
- You understand event design, identity and account mapping, data quality, and the limits of attribution. You can investigate a surprising metric before presenting it as fact.
- You’ve used product behavior to build lifecycle communications or activation experiments. You care about whether a message helps the recipient, not just whether it was sent or opened.
- You can work across product, sales, and marketing, translate a fuzzy problem into a testable change, and explain the result clearly.
- You pay attention to permissions, preferences, and the operational details that keep automations from contacting the wrong person or firing at the wrong time.
| We look at | We don’t weigh heavily |
|---|---|
| Systems you personally built and kept reliable | A long list of tools you’ve logged into |
| How you diagnosed a funnel problem and tested a change | Dashboard screenshots without a decision they informed |
| Quality of the customer experience and downstream outcomes | Messages sent, leads generated, or open rates on their own |
Experience with developer tools or product-led growth helps. The ability to learn a technical product, build the system, and question its results matters more.
The working reality
This is a hands-on role across engineering, analytics, and GTM operations. Some days you’ll write SQL or debug an event. Others you’ll build a workflow, review the copy in a triggered message, or sit with a salesperson to understand why an apparently qualified account was not a fit. You’ll work closely with product, growth & marketing, and sales.
There isn’t a settled funnel or a perfect data stack waiting for you. You’ll choose a small number of measures worth trusting, fix the plumbing behind them, and use them to make better decisions. You won’t be expected to own every piece of marketing or close deals yourself.
This probably isn’t the right role if you only want to administer a CRM, build dashboards on request, or set up automations without checking what happens to the people who receive them.
About neatlogs
neatlogs helps teams understand why their AI agents fail in production, fix the cause, and check whether the fix worked. It connects production traces with the judgment of the people who know what the agent should have done, then helps the team investigate what happened and decide what to change.
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