I’m hiring our first GTM Engineer at Lightdash - the Open Source Business Intelligence platform!

Jobs
1

We’re a YC company, last year Accel led our $11m Series A and we have an amazing team building the next generation of BI, driven by an Open Semantic Layer, and AI-first workflows.

Who you are

  • A creative generalist: comfortable at the intersection of code, data and customer engagement.
  • A pragmatic tinkerer: you’ve built scripts, mini-apps or tooling to solve real problems.
  • A commercial thinker: you grasp sales fundamentals and can articulate value to technical and non-technical audiences.
  • Data-obsessed: you set clear metrics, instrument every experiment and pivot when the evidence demands it.
  • A self starter: you decide how best to build and refine our growth stack, but we will provide as much mentorship and guidance as you need.
  • A fast learner: you’ll learn BI concepts on the fly and interrogate new domains with gusto.
  • Clay, n8n and Modern Data Stack experts are top of the queue.
  • You live in the UK and are willing to come into our new office in Central London roughly 1 day per week.

Areas of responsibility

  • Design and maintain end-to-end outreach workflows (e.g., Clay, n8n, Expandi, Zapier, Attio) to drive consistent lead flow.
  • Scrape, enrich and filter prospect data using AI techniques; optimise in real-time.
  • Formulate bold hypotheses, A/B-test messaging, channels and cadences; iterate rapidly on what works.
  • Partner with Sales, Marketing and Product to refine ICP criteria, shape go-to-market strategies and surface insights.
  • Sales Qualified Leads, pipeline value added—and continuously elevate performance.

KPIs owned by the role

  • Sales Qualified Leads
  • Pipeline value added

Apply here!

1 curtida
2

Great to see Lightdash investing early in a growth automation function.

One approach I have found reliable is to design outreach systems in three layers:

  1. Triggers – define the clear signal that moves a prospect into your flow (for example, an ICP match in Clay or an event from your product analytics).
    1. Processing – centralise data enrichment, message generation, and deduplication in a single n8n workflow so each prospect record stays clean and version-controlled.
    1. Monitoring – track success metrics in real time and route exceptions (bounces, API errors) to a review queue so issues never pile up.
      A couple of questions to shape architecture:
      • Roughly how many new prospects per week are you targeting initially?
      • Which existing data sources (CRM, product DB, third-party lists) must be integrated on day one?

Quick implementation tips:
• Start with a thin vertical slice (one trigger, one channel) and expand after metrics confirm lift.
• Keep data payloads uniform across Clay, n8n, and your sales tools to avoid brittle field mapping later.
• Automate opt-out handling early; it is easier than retrofitting after scale.

This is general guidance based on my experience with similar projects.

3

I think your AI script needs refining :slight_smile: this is off topic, irrelevant and your formatting is inconsistent.