GTM Engineer, Datasnipper
Intelligent audit platform
Be an early applicant
Intelligent audit platform
201-500 employees
Be an early applicant
201-500 employees
Company mission
To deliver intelligent automation with effortless data validation.
Role
Who you are
- 2-4 years in RevOps, marketing/sales/CS operations, data/CRM administration, or a closely adjacent role. Prior GTM Engineering experience is a plus, not a requirement - the strongest people in this discipline today are crossovers
- AI fluency you actually use. You've been integrating LLMs, automations, and agents into your work for a while. You can talk concretely about what works, what breaks, and what's still a hack
- A builder's mentality. When you hit a wall, you build the thing rather than file a ticket. You pick up Lovable, Bolt, n8n, or whatever new tool surfaces. You've shipped automations that touched real revenue
- Comfort owning infrastructure, not just automations. You own a data model, an integration, or a governance rule as confidently as you own the plumbing - not just what sits on top of it
- A commercial bias. You ask “does this help close a deal?” before “is this elegant?” You measure your work in meetings booked and pipeline influenced
- Cross-functional comfort. You can move between marketing, sales, CS, and engineering languages without losing the room
- CRM and data architecture: HubSpot
- Enrichment and orchestration: Clay, ZoomInfo, Apollo, Clearbit, Starbridge
- Integrations: Gong, Snowflake, Default AI, dialers, Common Room
- AI tooling: Claude Enterprise (Projects, Skills, and MCP integrations all unlocked), plus ChatGPT, and a flexible stack. Think: n8n, Lovable, Bolt
- Workflow automation: Zapier, HubSpot workflows
- Bonus: SQL, Python, or TypeScript. The real signal is willingness to learn by tinkering
What the job involves
- We're hiring a GTM Engineer to build and run the Data, Systems & AI Engineering layer underneath DataSnipper's GTM and Business Operations stack
- This is the build function: if RevOps decides what the ICP is, who owns which territory, and what counts as a qualified opportunity, this team turns those decisions into the systems that actually run them
- That's a bigger remit than any one tool
- You'll own the CRM data architecture, build the enrichment and signal infrastructure that feeds it, run the integrations and governance holding the wider stack together (Clay, Snowflake, Gong, and more), and build the automations and AI agents that turn all of it into something Marketing, Sales, CS, and BD run on every day
- You decide how the whole engine is put together, working across every function rather than waiting inside one for requests
- We are building this function from scratch, and therefore you will have the opportunity to lay out the foundations of what a world class AI GTM Engineering team can look like in a fast paced AI company
- We are looking for a team that can take and build upon the existing state- Marketing has built real foundation where enrichment runs steadily, AI nurturing agents are live and lifting reply rates, intent signals flow in from across the funnel, and HubSpot is trusted as the data model of record for accounts, contacts, deals, and lifecycle stages
- This team will be building the next next frontier: own integrations and vendor tooling (Gong, Snowflake, Clay, Default AI, Common Room, dialers) end to end instead of managing them reactively, and turn data governance - who can touch shared CRM objects, what change control looks like - from a principle into enforced practice
- Beyond that, there's a funnel zone each for Sales, CS, and BD still waiting to be built out, on top of the intelligence Marketing already relies on
- We need operators who are instinctively drawn to turning manual process into clean systems, and ungoverned data into governed infrastructure
- People who've spent their growth, marketing, sales, or RevOps careers quietly building automations and tightening the infrastructure underneath their own work, and are ready to do both full-time inside a function that already has shape and runway
- Administer and evolve the HubSpot data model: accounts, contacts, deals, and lifecycle stages
- Implement lead routing, ICP, and territory logic into the CRM per rules RevOps defines - you build it, RevOps owns what it means
- Implement rules of engagement (ROE) - handoff criteria and account rules - into CRM workflows, automation, and Clay infrastructure that execute them reliably
- Build and maintain enrichment via Clay and providers such as ZoomInfo, Apollo, Clearbit, and Starbridge
- Run TAM analysis and intent-data infrastructure, and keep data quality high through continuous enrichment workflows
- Decide which signals matter, how to surface them, and how to route them to the people who should act on them
- Be the mirror and advisor to the business teams through your work
- Own GTM tool integrations end to end (Gong, Snowflake, Clay, Default AI, Starbridge, dialers, Common Room, or equivalent) so data flows cleanly with no duplicate or conflicting records
- Apply data governance standards: review automation that touches shared CRM objects, and respect change control on the data model
- Shape vendor and tool decisions - fit, overlap, integration complexity - so we consolidate instead of running five tools for one job
- Build and maintain the core Clay & Lemlist infrastructure: account data, enrichment, sequences, templates, and how contacts flow in and out
- Develop account scoring models and standardized Clay functions that Sales and Marketing can use without building their own underlying workflows
- Build agents, automations, and AI-assisted workflows in Claude, Default AI, and connected tooling
- Marketing (inbound signal → MQL): inbound enrichment and routing, audience activation, intent signal infrastructure. Drive signal-based outbound - detect a buying moment (hiring, funding, leadership change, product usage shift), find the decision-maker, enrich, send
- Sales (SQL → Closed Won → Renewal → Expansion): prospecting automation, account planning tooling, rep efficiency. Build lifecycle expansion plays triggered by product signals so CS and AEs get prompted when an account is ready to expand
- BD (outbound signal → partner + BD pipeline): partner signal workflows, account intelligence, BD-specific outreach and pipeline automation
- CS (Closed Won → Adoption + Health): onboarding automation, health-score workflows, renewal and expansion signal tooling
- Work through a request → brief → build → feedback model. Ops people are the interface; they translate field needs into precise briefs you execute against
- Functions consume what you build rather than configuring or building it themselves, and feedback routes back through Ops - keeping your time focused on builds, not ad hoc asks or slides
Our take
With data having become a commodity in and of itself, infrastructure must be enforced so as to validate and contextualize it. Datasnipper does just that with its automated data validation platform for auditors.
Repetitive manual work in auditing means decreased efficiency and slower results. Auditors must reconcile data by cross-referencing it with that of older systems, verifying this data with documentation such as invoices, bank statements and previous contracts. Datasnipper’s platform uses intelligent automation that translates these documents directly into Excel spreadsheets, helping auditors make informed decisions at a faster rate.
Initially launched from Bootstrap, Datasnipper has successfully raised to Series B and has had a strong few years since. It doubled its revenue and customer base and reached Unicorn Status, indicating the auditing industry’s embrace of AI as a tool.
On top of this success, it recently announced a collaboration with Microsoft to bring AI agents into audit and finance workflows. The moves goes beyong incremental automation, positioning AI as a digital teammate embedded directly into everyday tools like Excel. With trust and adoption of AI continuing to rise across the sector, this partnership shows just how must of a leading role DataSnipper is playing in shaping the future of audit and finance.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (1 round)
Feb 2024
$100m
SERIES B
Total funding: $100m
Company HQ
Nieuwendammerham, Amsterdam, Netherlands
Leadership
Kai Bakker
(Co-Founder)Previously a Software Engineer at KAS BANK and ixly as well as a Freelance Web Developer.
Maarten Alblas
(Co-Founder)Previously a Software Engineer at Xomnia.
Jonas Ruyter
(Co-Founder)Previously a Senior Account Manager at Benify and an Account Manager at Sixt Rent a Car. They also worked in Marketing and Communication at IBM and was an Interaction Designer at ILUMY.
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