How do you translate a non-technical product vision into SLIs and SLOs?

WHAT THIS TESTS: This question evaluates whether you can bridge product strategy and site reliability engineering by translating ambiguous qualitative goals into rigorously defined quantitative targets. The interviewer wants to see that you understand SLIs must measure user-facing behavior, that SLOs should reflect user pain thresholds rather than system capacity, and that the process is iterative and collaborative rather than a one-time technical exercise.

A GOOD ANSWER COVERS: First, discovery with product and UX to identify critical user journeys and moments of delight or friction. Second, selecting SLIs that directly proxy the user experience such as request latency, error rate as yield, availability, or throughput rather than internal resource utilization. Third, setting SLO thresholds by observing or experimenting to find where users actually notice degradation rather than defaulting to 99.999 percent because it sounds good. Fourth, treating the SLO as an error budget that product and engineering jointly manage to balance velocity against reliability. Fifth, iterating as the product evolves because a new feature may introduce new user journeys that require new SLIs.

COMMON WRONG ANSWERS: A major red flag is jumping straight to infrastructure metrics like CPU utilization, memory usage, or disk IOPS as your primary SLIs without mapping them to user-perceived outcomes. Another red flag is choosing 99.9 percent or 99.99 percent availability arbitrarily without user research or historical data to justify the number. A third warning sign is treating SLOs as static guarantees rather than evolving targets that require cross-functional negotiation.

LIKELY FOLLOW-UPS: The interviewer may ask how you would handle a service that lacks direct client-side telemetry and must rely on server-side proxies. They might probe how you prioritize competing SLOs when multiple user journeys have conflicting latency and throughput needs. They could also ask what you do when the product team wants to launch a feature that would consume the entire error budget, forcing a discussion of trade-offs and launch gates.

ONE CONCRETE EXAMPLE: Suppose the vision is a delightful checkout experience. The critical user journey is completing a purchase. The SLI is the fraction of checkout requests that succeed and return within two seconds, measured at the edge if client telemetry is unavailable. The SLO is set to 99.5 percent success with a 95th percentile latency under two seconds based on checkout abandonment data showing sharp spikes after three seconds. The error budget allows 0.5 percent of requests to fail or slow down each quarter, giving the team room to deploy new payment methods without immediately violating reliability targets. If a new fraud-check service pushes latency to four seconds, the team can point to the SLO and the abandonment data to justify delaying launch or investing in async processing.