At a Glance
- Tasks: Build and maintain data pipelines for prospecting and CRM management.
- Company: Join a cutting-edge AI research tool company trusted by 2,000+ firms.
- Benefits: Flexible work environment, competitive salary, and direct impact on company growth.
- Other info: Opportunity to innovate without bureaucratic hurdles in a small, well-funded team.
- Why this job: Be at the forefront of data-driven sales operations in a dynamic team.
- Qualifications: 3-5 years in revenue or sales operations with strong data skills.
The predicted salary is between 45000 - 55000 £ per year.
Nylon builds AI research tools that are changing how lawyers and accountants work. 2,000+ firms across New Zealand, Australia and the UK trust our platform, from the biggest firms in the world to small specialist practices. Our go-to-market team runs on data. The quality of our outreach lists, the accuracy of our CRM and the reliability of the systems that connect them decide how well our sales and marketing efforts perform. We're hiring a GTM Engineer to own that data layer.
The role involves building and maintaining the pipelines that find, enrich and deliver prospect data to our sales team, keeping the CRM clean and trustworthy, and connecting the tools in our stack so information flows without manual effort. It's a hands-on technical role at the intersection of sales operations, data and automation. You'll report to our Head of Go-to-Market, work closely with our account executives, SDRs and marketing, and have a lot of freedom in how you solve problems.
What you'll be doing:
- Outreach data and list building: Building targeted prospect lists across NZ, AU and the UK from the ideal customer profiles the GTM team defines, covering firm type, size, practice area, location and seniority.
- Sourcing data from multiple channels, including scraping public sources, enrichment platforms (Clay, Apollo or similar), LinkedIn Sales Navigator, professional body registers and our own product and website data.
- Designing waterfall enrichment flows that verify contact details, fill gaps and score leads, keeping bounce and unsubscribe rates low.
- Using AI tooling to research accounts at scale, qualify fit, personalise outreach inputs and automate the repetitive parts of list building.
- Maintaining data quality, deduplication and consent standards so lists arrive usable and compliant with the Privacy Act, Australian Privacy Principles and UK GDPR/PECR.
CRM and integrations:
- Owning the health of our CRM (Attio), including object and field design, pipeline stages, data hygiene, deduplication, ownership rules and reporting views.
- Building and maintaining integrations between the CRM and the rest of the GTM stack, including our dialler (JustCall), scheduling (Calendly), billing (Stripe), analytics (Metabase and our product database) and marketing channels (Meta Ads, website forms).
- Writing automations and scripts (Python or JavaScript, webhooks, Zapier/Make/n8n or direct API work) so leads, activities and revenue events land in the right place without anyone re-keying them.
- Querying our data warehouse and Metabase in SQL to report on pipeline, conversion, attribution and rep activity, and to reconcile CRM records against source-of-truth systems.
- Documenting how the stack fits together and training the team on the parts they touch.
About you:
- 3 to 5 years in revenue operations, sales operations, growth engineering, data or a similar role, ideally at a B2B SaaS company.
- Proven experience building outreach or prospect lists with scraping and enrichment tools such as Clay.
- Strong CRM administration on at least one modern platform (Attio, HubSpot, Salesforce or similar), including data model design and hygiene rather than just day-to-day use.
- Solid SQL and comfort in a BI tool such as Metabase.
- Working proficiency in Python or JavaScript, plus experience with REST APIs, webhooks and automation platforms.
- Practical experience using LLMs and agentic tooling in real workflows, with a clear view of where they help and where they don't.
- A high bar for data quality and the discipline to keep systems tidy over time.
- Clear written communication, translating what sales and marketing need into systems that deliver it.
- Experience with accounting or legal firms, or professional body datasets in NZ, AU or the UK, is an advantage.
- Familiarity with marketing attribution and multi-touch pipeline analysis is an advantage.
- The right to work in the UK.
What you'll get:
- No legacy stack to work around and no committee to get through. If you can show a better way of finding customers or running the CRM, you'll ship it.
- A small, well-funded team where the systems you build directly determine how well the company grows.
GTM Engineer in Brighton employer: NYLON
At Nylon, we pride ourselves on being an innovative employer that empowers our team members to drive meaningful change in the legal and accounting sectors. As a GTM Engineer, you'll enjoy a collaborative work culture that values data-driven decision-making and offers ample opportunities for professional growth, all while working with cutting-edge AI tools in a dynamic environment across New Zealand, Australia, and the UK. With no legacy systems to hinder your creativity, you will have the freedom to implement solutions that directly impact our success and shape the future of our platform.
StudySmarter Expert Advice🤫
We think this is how you could land GTM Engineer in Brighton
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We think you need these skills to ace GTM Engineer in Brighton
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
