“Use numbers on your resume” is standard advice at this point — and for good reason, given how much it helps a bullet point land. But it creates a real problem for a lot of data professionals: what if you genuinely never tracked the metrics that would make a strong bullet point?

Maybe you built a pipeline nobody ever measured the before/after impact of. Maybe you worked at a small company with no formal reporting on cost savings or time saved. This is more common than most resume advice acknowledges — and it doesn’t mean you have nothing to show. It means you need proxy metrics instead of ROI figures.

Proxy metrics: what to use when you don’t have a direct number

Scale

Even without a measured outcome, you almost always know the scale of what you worked with — data volume, number of tables, number of users affected, number of pipelines maintained. “Maintained pipelines processing 200GB of data daily” is a legitimate, specific claim, even without a before/after comparison.

Frequency

How often did the thing you built get used? “Built a dashboard used weekly by the 8-person operations team” tells a real story of adoption and relevance, even with no dollar figure attached.

Team size and scope

If you led or contributed to something involving multiple people, that’s a legitimate scale indicator. “Collaborated with a 5-person cross-functional team to launch…” signals scope of responsibility even without a performance metric.

Time-based comparisons

Even if you never tracked cost savings, you likely have a rough sense of time: how long something used to take manually versus after you automated it, or how quickly you were able to complete a project versus a typical timeline. “Reduced manual reporting time from roughly a day to under an hour” is a legitimate estimate, not a fabrication, as long as it’s genuinely accurate to your recollection.

Before/after state, even without percentages

Not every improvement needs to be expressed as a percentage. “Eliminated a recurring data quality issue that had caused monthly reporting delays” describes real impact without needing an exact numerical reduction.

What NOT to do: don’t fabricate false precision

There’s a difference between reasonable estimation and making up numbers you can’t stand behind. If you genuinely don’t know whether something reduced costs by 20% or 35%, don’t guess a specific figure to sound more impressive — a false, overly precise number that falls apart under a follow-up interview question (“how did you calculate that?”) does more damage than a more honest, scale-based description. If you’re estimating, it’s fine to use qualifying language: “an estimated,” “approximately,” or “roughly” — this is more credible than false precision, not less.

A practical exercise: reconstructing metrics after the fact

If you’re rewriting a resume for a job you left months or years ago, you can often reconstruct reasonable estimates even without having tracked them in real time:

• Check old tickets, PRs, or project docs for scope details (how many tables, how many users, how long a migration took)
• Ask a former teammate or manager if they recall a rough before/after comparison
• Estimate conservatively based on what you do remember, rather than guessing high

This isn’t about inventing achievements — it’s about surfacing real scope and impact that you simply never formally measured at the time, which is an extremely common gap, not a personal failure.

When scale or scope genuinely isn’t impressive — reframe, don’t inflate

Sometimes the honest answer is that a project was small in scope. In that case, the better move isn’t to inflate a small project into something it wasn’t — it’s to choose a different aspect of the work to highlight: the technical complexity involved, a skill you built, or a process improvement that will matter more to a future employer than the original project’s scale did.

The bigger point

A resume with realistic proxy metrics and honest scope descriptions is stronger than one with either vague “responsible for” language or invented statistics that won’t hold up under scrutiny. Precision matters, but precision means accuracy, not decimal points.

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Ready to put this into practice? See our bullet point formula guide for the exact structure to build these into strong resume lines.