GTM / Growth Engineer

About the role

This position owns the lead list side of structured growth experiments for enterprise operations teams, translating hypotheses into handoff-ready lists while sales owns execution. You will be responsible for ensuring that every list is built on a clear, documented hypothesis and that the output is reliable and reproducible from start to finish. The role requires you to bridge product intuition with data rigor so that each list can be evaluated and improved over time. You will work closely with sales to understand their needs and translate ambiguous growth questions into precise list definitions. Your focus will be on quality, traceability, and the ability to iterate based on what the data reveals. You will spend time designing list logic, validating sources, and documenting the reasoning behind each list you produce. Strong written communication will allow you to make your reasoning explicit so that others can understand and trust your lists.

Key facts

What you'll do

Design and own the hypothesis definition process for lead list experiments, ensuring each list has a clear purpose and success criteria.

Translate ambiguous growth questions into precise list specifications that sales can execute without further clarification.

Use data and tooling to source, validate, and enrich lead data, maintaining a high standard of list quality and accuracy.

Build reproducible list workflows that can be revisited, compared, and improved across experiments.

Collaborate with sales to understand their evolving needs and adjust list definitions to maximize handoff value.

Implement checks and documentation that make list logic transparent and auditable over time.

Monitor list performance, surface insights, and propose adjustments based on observed outcomes and new hypotheses.

Own the end-to-end quality of lists you create, from initial hypothesis through to the final handoff to sales.

Write clear documentation for each list, including the reasoning, data sources, and assumptions behind its construction.

Partner with engineers and sales to standardize best practices for list creation and experimentation.

Continuously refine your methods by testing new sourcing strategies and measurement approaches for list effectiveness.

Support the creation of training materials that help sales understand how to use and interpret each list correctly.

Contribute to the development of internal tools that improve the efficiency and reliability of list building.

Act as the primary owner for the list side of growth experiments, driving accountability for results and learnings.

Maintain a structured archive of past lists and outcomes to enable learning and avoid repeated mistakes.

Requirements

Background in sales research, SDR, or data enrichment within B2B SaaS is required, without mandating formal engineering skills.

You must be comfortable working with data sources and platforms used for sourcing and enriching lead data in a B2B context.

Strong attention to detail is required to ensure that lists are accurate, complete, and aligned with the original hypothesis.

You must be able to communicate clearly in writing, explaining the rationale and scope of each list to both technical and non-technical stakeholders.

You must be comfortable working asynchronously and managing your schedule in a remote, distributed team environment.

You must be based in the United States and able to work full-time under an employment arrangement that complies with local regulations.

You must be able to take ownership of ambiguous problems and define the structure and success criteria for your work.

You must be willing to iterate on your work based on feedback from sales and observed performance of the lists you create.

You must be able to translate high-level growth goals into specific, testable hypotheses that can be turned into actionable lists.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down unfamiliar problems, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Work in this field involves using data and tooling to define and test hypotheses for growth experiments.

Common tools include Clay, Apollo, and similar platforms for sourcing and enriching lead data.

The role focuses on list quality and reproducibility rather than outbound execution.

Clarity in hypothesis definitions improves list effectiveness over time.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.