AMA: LinkedIn Director of Sales Strategy and Operations (EMEA & LATAM), Eduardo Moreira on Revenue Ops Interviews

An interview is your chance at presenting as both highly effective (having the right skills and behaviours), and balanced (big picture vs. technical depth). To me, the key pitfalls to avoid are: * Failing to show organizational navigation skills. When discussing past experience, always articulate: (1) Where was the ship sailing? - how did your work fit into the company’s broader strategy? (2) What was your role on the command deck? - what were you, specifically, responsible or accountable for? Be ready to articulate both dimensions for to any past work discussion. * Being too literal — missing the “question behind the question”. Interviewers rarely care about your ability to navigate your current/past employer's tech stack blind spots or cross-functional complexities; they’re looking for success predictors such as curiosity, structured thinking, resilience, and stakeholder management. Read between the lines and solve for the real question. * Not tying experience to success in the new role. Don’t wait until the last 2 minutes to make your case. Identify beforehand the 2-3 key aspects that make you the best fit for the position, and deftly smuggle them into your answers throughout the conversation. * Also, don't forget the small stuff that makes a big difference: * Time management: Have a 15-second, 2-minute, and 5-minute version of every key story. Layer your comms and gauge demand for longer versions as you go. * Jargon overload: Unless it’s universal (ROI, ARR, CAC, LTV), explain it quickly or ditch it entirely.

RevOps autonomy varies with role and company, but workbook scope, alumni trajectories, task assignment and performance management provide valuable signals. To gauge autonomy I'd focus on 3 things: * Obtain role scope detail with smart questions. RevOps is a broad container term, and the role may or may not align with your topics of expertise. Asking thoughtful questions about book of work, team vision, roles and responsibilities and partnership model with other teams not only clarifies expectations but also signals your ability to navigate uncharted territory. * Look at past career trajectories. Ask how previous holders of the position evolved - did they gain more responsibility, make cross-functional jumps, or hit a ceiling? The growth of past team members tell you a lot about real autonomy. * Probe task assignment philosophy and performance evaluation/OKRs. True autonomy lives in the middle. If processes are overly rigid, this may mean that you’re going to be autonomous, but only within a narrow, predefined scope. A loose or absent process? That’s not real autonomy - it just mean you're likely to be left to chase shifting leadership priorities.

Great interviews come down to structured thinking, deep research, and tactical preparation. * Use structured but tailored problem-solving. Have a repeatable method for top-down and bottom-up problem solving. Master it by repetition until it is clear and hypothesis-first. After that, learn to tailor your approach: generalists will value a consistent and logically sound playbook (e.g. 80/20, MECE -- think strategy consulting), finance leaders will look for your judgment on margins/growth, sales leaders will want a take including the customer lens. Know your audience. * Study the job description like a playbook. Read it multiple times. Use AI to compare it against similar JDs from the company and industry - spot standout items to the vocabulary level. JDs are often templated, but intentional wording can reveal what the hiring manager values most. * Research the hiring team. Websites, mission and vision are only page 1. Skip any unsolicited outreach and instead, use LinkedIn posts, team backgrounds, research/white papers published, event appearances, and keynotes to get a sense of their priorities and culture. * Familiarize yourself with the tech stack. If you don’t already know the tools, invest time in YouTube demos or product walkthroughs. Even a high-level understanding can help you sound sharper in discussions, especially if you're switching verticals or trying to break into RevOps.

The best candidates are able to successfully demonstrate traits and mental shortcuts of the best professionals. Based on my RevOps experience, the best professionals are those who thrive in complexity, drive business momentum, and focus on impact over ego, with a few common characteristics: Curiosity for untangling complexity: they see broken processes, faulty/missing data and ambiguity not as deterrents or negativity fuelers, but as clutter they aspire to remove to reveal business-propelling insights; High-energy problem solvers: they grab the marker, start the whiteboarding, and keep things moving fast; Low tolerance for politics/ego: they prioritize fixing the go-to-market engine over personal wins and avoid fostering or entertaining land-grab behavior; Answer-first thinking: they take intelligent risks when problem-solving, and don't shy away from making smart assumption-based calls on key metrics; Clarity on fit: they don’t chase just any job; they look for a role that makes them a better version of themselves.

I’ve been in all four quadrants - I've gone “above and beyond” and both landed the job and been rejected, and I’ve also "played it safe" with mixed results. Each outcome taught me something. The key litmus test to me is understanding seniority and structure - done right, it’s a differentiator; done wrong, it can backfire. Seniority: The more senior the role, the more this is important - even expected. Deep research helps you speak the company’s language more fluently, anticipate challenges and bring novel ideas. If your interviewer doesn’t bite when you mention your prep, attach it to your thank-you note as a subtle differentiator. Structure: In highly structured or mature organizations, unsolicited deep dives can signal misalignment between your expectations and that of the hiring team. Worst case, it can be read as a tendency to “boil the ocean”. Use your recruiter or TA contact to gauge if this approach will be well-received.

When hiring someone without RevOps experience, I focus on transferable skills that predict success: * Analytical, Data-Driven Thinking – Can they break down complex problems logically? A good test is a case study with simple and well documented data that requires idea generation, prioritization, numerical reasoning, and recommendations. Dry-run it with high performers, mentors, and non-RevOps folks. * Curiosity – Do they have a strong drive to learn and a structured approach to self-teaching? Ask: “Tell me about a time you learned something difficult on your own.” or “What’s the last skill you taught yourself?” Look for perseverance, resourcefulness, and efficiency. * Communication & Stakeholder Management – Can they convey ideas clearly and influence decision-makers? An interview with a senior non-RevOps leader helps assess clarity and conciseness. You can also use conflict resolution questions (even from academic settings) or ask for comms approach in "tricky" scenarios (e.g., giving tough feedback, owning a model mistake, troubleshooting a tech issue). * Adaptability – Are they comfortable with ambiguity and shifting priorities? Add plot twists to the case study or ask counterfactuals like, “Would your answer change if growth was the only goal, not margin?” Having candidates walk through a major professional/academic change and how they handled it can be highly valuable as well. Dig deeper into their thought process through uncertainty. Quick note on assignments: in my experience those work best to set the scene, not as "take-home exams". Treat them as step 1 of a business case. For example, provide a dataset and ask for 3 insights + 3 follow-up questions as output. This helps assess data hygiene and hypothesis formulation, but the real value is in the live discussion: dive into problem-solving choices and logic pathways. Use extra data live in the interview to pressure-test assumptions, explore second-order effects, and test their comfort in pivoting on new data.