Technical practice
How would you turn an ICP definition into usable GTM segments?
What to coverThe role includes architecting data flows from ICP definition through segmentation. Suggested preparation (inference): explain how you would select attributes, handle missing data, and check whether segments are useful for prospecting and routing.
How would you design a Clay waterfall-enrichment workflow?
What to coverThe posting specifically calls for Clay proficiency, waterfall enrichment, and GTM signal activation. Suggested preparation (inference): describe source order, fallback logic, deduplication, and how you would assess coverage and data quality before activating a signal.
How would you connect prospecting signals to outbound sequencing?
What to coverRevX names Apollo, prospecting, and signal-driven outreach as part of the work. Suggested preparation (inference): discuss what makes a signal actionable, how contacts enter a sequence, and how you would avoid irrelevant or duplicate outreach.
How would you integrate HubSpot or Salesforce with another revenue tool?
What to coverThe role calls for CRM integrations using APIs, automation platforms, or scripts. Suggested preparation (inference): walk through field mapping, record matching, authentication, error handling, and how you would detect and recover from failed syncs.
How would you design lead scoring, routing, and handoff as one data flow?
What to coverScoring, routing, and handoff are explicit responsibilities. Suggested preparation (inference): explain the inputs and decision points, ownership of each stage, and how you would test ambiguous or incomplete records.
How would you evaluate an LLM agent for sales enablement or SDR automation?
What to coverThe posting calls for developing and prompt-engineering AI agents for both uses. Suggested preparation (inference): describe prompt and context design, representative test cases, human review points, and criteria for deciding whether an output is usable.
How would you use AI to improve CRM hygiene without introducing bad data?
What to coverAI agents for CRM hygiene are part of the advertised work. Suggested preparation (inference): distinguish tasks suitable for automated cleanup from those needing review, and explain validation, auditability, and correction of erroneous updates.
How would you assess whether a pipeline-management automation is working?
What to coverAutomating pipeline management is a stated responsibility. Suggested preparation (inference): define the workflow's intended change, examine adoption and data quality alongside pipeline measures, and distinguish automation failures from process issues.
When would you use an API script instead of Zapier, Make, or another low-code tool?
What to coverThe role explicitly supports APIs, custom scripts, and no-code or low-code automation. Suggested preparation (inference): compare control, maintainability, monitoring, failure recovery, and the complexity of the data flow rather than assuming one approach is always best.
Behavioural practice
Tell me about a time you translated a GTM team's problem into an automation plan.
What to coverThe role involves partnering with RevOps consultants and client GTM teams to identify opportunities. Suggested preparation (inference): use a real situation, your discovery and prioritization actions, and the resulting decision or outcome.
Describe a time stakeholders disagreed about a revenue-process change.
What to coverCross-functional work across tools, data, and processes is central to this role. Suggested preparation (inference): give a real situation, how you surfaced trade-offs and reached a decision, and what happened afterward.
Tell me about an automation that failed or produced unexpected data.
What to coverBuilding and maintaining revenue-tool integrations is an explicit responsibility. Suggested preparation (inference): explain the actual failure, your troubleshooting and communication, and the result or preventive change.
Describe a time you handed off a technical workflow to another team.
What to coverThe posting calls for documenting systems, playbooks, and technical architecture for client handoffs. Suggested preparation (inference): describe the situation, what you documented or demonstrated, and how you determined the receiving team could use it.
Tell me about a time you evaluated a new AI or GTM tool for practical use.
What to coverThe role asks for staying current with evolving AI and GTM tooling and bringing useful approaches to engagements. Suggested preparation (inference): use a real example showing the need, your evaluation actions and trade-offs, and the outcome without overstating its impact.