GTM Engineer

The GTM Engineer, or Go-to-Market Engineer, builds and maintains automated sales prospecting systems. They connect account and contact data, data enrichment and artificial intelligence with the tools used by salespeople. Their work helps prepare research, prioritise prospects and trigger actions according to explicit rules.

The role described here focuses on outbound prospecting. The GTM Engineer turns a sales requirement into a sequence of processing steps, known as a workflow: collecting information, enriching it, checking it and then passing it to the right tool or person. They also build the necessary connections and monitor how they operate after launch. Delivering a system that salespeople do not use is therefore not enough to define their contribution.

The role sits at the intersection of sales, marketing and RevOps teams. To establish its reporting arrangements, specify who chooses prospecting priorities, who approves sales rules and who helps the postholder resolve technical difficulties. Responsibilities must remain clear when several teams use the same data.

GTM Engineer, RevOps and Sales Engineer: who does what?

• The GTM Engineer builds the prospecting system and its automations, from data enrichment through to its use in sales.
• Sales Ops / RevOps have a broader remit in organising revenue-related activities. These teams may work with the GTM Engineer, particularly on data and tools.
• The Sales Engineer, or pre-sales engineer, helps prospects and customers understand the technical aspects of the product being offered.

The job title alone does not always define the role. At Attio, a variant of the role works on customers’ systems after the sale. When recruiting for the scope described here, therefore, make building the internal prospecting system an explicit part of the expected responsibilities.

The first challenge is to turn a sales intention into usable rules. A request such as “target our prospects more effectively” leaves several decisions open: which companies should be selected, what information should be researched and what action should be triggered next? Without these choices, an automation may work technically without providing salespeople with relevant accounts.

Data reliability determines the reliability of subsequent processing. Duplicates, an incorrect match between a contact and a company, or unverified enriched information may end up in the CRM and in drafted messages. The GTM Engineer must be able to identify these discrepancies, understand their origin and correct the flow of information.

Connecting signals to sales decisions

A detected event is not a guarantee of a purchase. Recruitment or fundraising can serve as triggers, but their relevance depends on the target audience and the offering. The challenge is to test whether this information actually helps salespeople start useful conversations. The volume of accounts processed does not, on its own, answer that question.

Fictional example: a company wants to contact accounts that are recruiting. The system detects the event, enriches the available information and prepares a message. Before expanding the process, the team checks whether the selected companies match its target audience and whether the message accurately describes their situation. It retains human review where information remains uncertain.

Planning for operation after launch

Choosing the postholder also means planning for maintenance: diagnosing errors, monitoring unexpected costs, updating connections and handing over rules. Someone who can put together a demonstration but cannot explain anomalies may leave the company dependent on a fragile system.

Finally, agree with salespeople what will determine whether to continue, change or stop a test. The conversations generated and the progress of opportunities give experiments a commercial purpose, without guaranteeing a result in advance.

• Work with sales teams to define the targets, processing rules and expected outcomes of a prospecting workflow.
• Assemble and enrich account and contact data to prepare it for use in sales.
• Deduplicate records and define rules for classifying and assigning prospects.
• Connect selected signals to prospecting actions and test their relevance.
• Connect the CRM, data sources and sequencing tools.
• Build prompts to produce research and draft messages whose content can be checked.
• Monitor errors, fix integrations and keep automations running.
• Compare experiments based on sales results and adapt processing steps.
• Document workflow rules, changes, tests and recovery procedures.

Backgrounds in sales operations, growth or technical roles can provide preparation for this work. Their value depends on the skills acquired: understanding prospecting, handling data and building connections between tools.

For someone coming from sales operations, technical learning focuses on building and diagnosing automations. Someone from a technical background benefits from understanding sales work: choosing accounts, using information and how teams use the results. A growth background can bring experience of testing, which needs to be accompanied by skills in making integrations reliable and maintaining them.

Practical learning covers areas including enrichment, prompts, signals and CRM data quality. Clay University offers modules on these topics. This vendor training is one example of a resource, without being a mandatory course or sufficient to establish mastery of the role.

The foundations of GetPro’s assessment approach

GetPro structures assessment around a framework that prioritises the criteria for the role and specifies how each will be assessed. It distinguishes between aspects that can be verified from a candidate’s background and those that require a discussion or a test. In interviews, open questions and concrete examples help explore the key criteria in greater depth. Reference checks help cross-check responsibilities and shed light on areas that still need further exploration.

Advice on assessing a GTM Engineer

The criteria and exercises below are suggestions to adapt to the role. They apply these foundations to the GTM Engineer role and do not constitute a specific protocol used by GetPro.

1. Define the expected level of autonomy

Describe the systems to be built, the existing tools and the sales users. Distinguish between configuration work, integrations to be developed and maintenance to be carried out. Set the assessment criteria before interviews.

Choose observable criteria: data quality, clarity of rules, reliability of connections, checks on AI outputs and interpretation of sales results. Weight them according to the work actually assigned.

A candidate who identifies missing information shows how they scope a problem. If an answer focuses solely on tool brands, explore the candidate’s reasoning further.

2. Examine a system already built

Ask the candidate to present a project, specifying their personal contribution. Have them explain the input data, processing steps, connected tools and resulting actions. Examine how salespeople used the output.

Revisit an incident: how did they detect and then resolve it? Ask what changed after launch and how those changes were documented.

An explanation that connects technical choices to sales use is a positive signal. A demonstration without an example of maintenance or an identifiable personal contribution calls for further questions.

3. Propose a limited prospecting exercise

Fictional example: provide account records containing duplicates, missing information and a recruitment signal. Ask the candidate to design how they should be processed before being passed to salespeople.

Have them specify the matching rules, the information to enrich and the prioritisation conditions. Ask which connections are needed, how to check an AI output and where to include human approval.

Then introduce a connection error or an unverifiable result. Observe how they diagnose the problem and prevent incorrect processing from continuing. Do not require a particular brand if proficiency in it is not necessary for the role.

A good answer sets out the system’s assumptions and limitations. Be concerned if the candidate treats every signal as a certain intention to buy or passes on information without checking it, even though they cannot verify it.

4. Connect tests to sales results

Ask which results would justify keeping or changing the workflow. Have the candidate distinguish between the volume of records processed, their use by salespeople and the progress of opportunities.

Invite the candidate to explain how they would compare two data sources or two selection rules. Look for an approach that helps explain the differences observed. A promise of performance without an explicit hypothesis is not enough.

5. Assess collaboration and handover

Have the candidate present the choices made in the practical exercise to a sales colleague. Assess mutual understanding: does the candidate explain the technical limitations and specify which decisions belong to the business?

If the role includes management, assess the candidate’s actual management responsibilities separately. Do not infer an ability to manage people solely from proficiency in automation.

If you do not have the technical expertise in-house, involve someone who can examine the integrations and diagnosis. Retain a sales colleague to assess the usefulness of the results.

6. Cross-check responsibilities held

During reference checks, revisit the projects presented and the candidate’s personal role. Ask how the systems were used, maintained and handed over.

Compare these findings with the initial criteria. Distinguish between a demonstrated skill, a skill that needs further exploration and a skill that could not be observed, without turning a lack of information into a definitive conclusion.