Data Analyst
Resume Metrics

The Numbers Recruiters Look For

The Data Analyst resume metrics that earn a read: which numbers to use, what good looks like, and where to find each one. Built from 12 years of recruiting, including many years at Google.

Emmanuel Gendre, former Google Recruiter and Tech Resume Writer

Authored by

Emmanuel Gendre

Tech Resume Writer

Last updated: June 4th, 2026 · ~10 min read

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Ex-Google Recruiter
Emmanuel Gendre, former Google Recruiter and Tech Resume Writer

From the author

Emmanuel Gendre, ex-Google recruiter

A recruiter's opinion on data analyst resume metrics

Every resume guide gives the same advice: put a number on what you did. For a data analyst it ought to be simple, the job is numbers all day, yet most analyst resumes just name the tools and stop.

So which numbers truly merit a spot on a data analyst resume? And what is the source for each one? Does any of it really tip a hiring call?

Across my recruiting career, a fair stretch of it at Google, the analysts who landed offers showed the work led somewhere: not “built dashboards in Tableau” but “built the dashboard that cut churn 7 points.” That second version wins a callback, because anyone can pull a chart, few can prove it moved a decision.

Sorting out which figures matter, then writing them so a recruiter feels their weight, makes up the core of what my resume writing service does. This page runs through each number worth listing on a data analyst resume: when it fits, where it tends to live, and how to phrase it in one bullet.

Want a quick read before that? Send it my way and I will take a look, free.

Start here

Why metrics matter on a Data Analyst resume

I map out the full hiring sequence in my piece on how recruiters screen resumes, and it moves in phases. A recruiter takes the opening rounds, a rapid scan of your profile summary, then your recent roles. Only then does a senior analyst or the hiring manager dig into the details and decide whether you can genuinely turn data into decisions.

So two readers weigh your figures: first the recruiter, then an analyst who can size up precisely what a 7-point churn drop or a 99.8% accurate report really demanded.

A recruiter does not study the figure; they are chasing keyword matches. The analytics lead you would sit under reads “cut churn 7 points” and immediately sees the effort it took. What a real number gets you is this: it proves you turn data into decisions, not just fire off the odd SQL query.

Each one pulls a different amount of weight, though. And when yours come out small, do not stress: for a data analyst, one solid impact or adoption figure already sets you above the spreadsheet-jockey crowd.

Roughly, here is how much each of the three counts:

The logic

Which types of metrics to use
for a Data Analyst resume

Spend time in the Job Search Toolkit and you know each resume I write begins from a role profile. Quick reminder: a role profile is the bundle of competencies a job is built to hire on.

Recruiters score you straight off it. The data analyst resume guide covers what each section needs.

Every area of the data analyst profile belongs on the page, best inside your current role, sitting next to the figure that backs it.

Those are the metric types. A data analyst gets six, one for every corner of the role. They are:

The full list

The full list of Data Analyst resume metrics

Six families, and inside each, the five figures a hiring manager weighs most, in order. Each one lays out what it captures, the average, good, and great mark, how to read it, plus an example bullet to adapt. Nearly all live in tools you keep open anyway: your BI app, the warehouse, your SQL editor, and product analytics. The Data Analyst resume skills page lists the rest.

1

Business & Decision Impact

An analysis nobody acts on is a slide deck. These are the numbers that tell a hiring manager your work moved a decision, not just described one.

Revenue influenced

Revenue your analysis helped drive or protect.

Benchmark

Average$100k
Good$1M
Great$10M+

Measure with

Tableau Looker

Example bullet

Sized the pricing change that added $2.4M in annual revenue.

Cost saved

Spend your analysis helped cut.

Benchmark

Average$50k
Good$500k
Great$2M+

Measure with

Power BI Excel

Example bullet

Found the spend leak that saved $480k a year in vendor cost.

Decisions informed

Calls leadership made off your work.

Benchmark

Averagea few
Goodteam-wide
Greatexec-level

Measure with

Tableau Looker

Example bullet

Built the analysis that settled the market-expansion call in the quarterly review.

Churn / retention impact

Customer retention your insight moved.

Benchmark

Average+2pts
Good+5pts
Great+10pts

Measure with

Amplitude BigQuery

Example bullet

Pinpointed the churn driver that, once fixed, lifted retention 7 points.

Conversion lift

Funnel improvement your analysis drove.

Benchmark

Average+5%
Good+15%
Great+30%

Measure with

Google Analytics BigQuery

Example bullet

The funnel analysis behind a checkout fix lifted conversion 12%.

2

Dashboards & Reporting

A dashboard nobody opens is wasted work. These show you build reporting people rely on, and that you took manual work off someone's plate.

Dashboard adoption

Weekly users on the dashboards you built.

Benchmark

Average20
Good200
Great1k+

Measure with

Tableau Power BI

Example bullet

Built the exec dashboard 120 people open every Monday.

Reports automated

Manual reports you replaced with self-refresh.

Benchmark

Averagea few
Gooddozens
Greatall of them

Measure with

Looker BigQuery

Example bullet

Automated 30 weekly reports, ending the Monday-morning copy-paste.

Reporting time saved

Hours of manual reporting you removed.

Benchmark

Average5 hrs/wk
Good20 hrs/wk
Greatan FTE

Measure with

Power BI Excel

Example bullet

Cut reporting time from 15 hours a week to under one.

Self-serve coverage

Share of questions answered without an analyst.

Benchmark

Average20%
Good50%
Great80%

Measure with

Looker Metabase

Example bullet

Took the team self-serve on 70% of routine questions.

Refresh latency

How fresh the numbers in your dashboards are.

Benchmark

Averageweekly
Gooddaily
Greatnear real-time

Measure with

BigQuery Snowflake

Example bullet

Moved the KPI dashboard from weekly to near real-time.

3

Experimentation & A/B Testing

Anyone can ship a change; an analyst proves it worked. These show you run experiments that hold up, not a before-and-after with no controls.

Experiments run

A/B tests you designed or analyzed.

Benchmark

Average5
Good30
Great100+

Measure with

Amplitude Python

Example bullet

Ran 40 A/B tests in a year, killing the ideas that did not move the metric.

Experiment win rate

Share of tests with a real, shipped lift.

Benchmark

Average15%
Good30%
Great45%

Measure with

Mixpanel Python

Example bullet

Got the experiment win rate to 35% by tightening hypotheses up front.

Conversion lift proven

Biggest validated lift from a test.

Benchmark

Average+3%
Good+10%
Great+25%

Measure with

Google Analytics BigQuery

Example bullet

Proved the checkout test that lifted conversion 14%, significant at 95%.

Statistical rigor

Whether your calls are powered and significant.

Benchmark

Averageeyeballed
Goodsignificant
Greatpowered + significant

Measure with

Python pandas

Example bullet

Set the power and significance bar every test now clears before shipping.

Time to readout

How fast you turn a test around.

Benchmark

Averageweeks
Gooddays
Greatsame day

Measure with

Amplitude BigQuery

Example bullet

Cut experiment readout from two weeks to two days with a templated analysis.

4

Data Quality & Trust

A number people do not trust is worse than no number. These show you ship analysis that holds up to scrutiny, and that you fixed the data nobody believed.

Report accuracy

Share of your numbers that hold up on audit.

Benchmark

Average95%
Good99%
Great99.9%

Measure with

BigQuery Python

Example bullet

Got report accuracy to 99.8% after rebuilding the metric definitions.

Standard definitions

Shared metric definitions you set so numbers match.

Benchmark

Averagea few
Goodteam-wide
Greatcompany-wide

Measure with

Looker BigQuery

Example bullet

Wrote the metric layer so revenue meant the same thing everywhere.

Discrepancies cut

Reporting mismatches you eliminated.

Benchmark

Average-50%
Good-80%
Great-95%

Measure with

Python BigQuery

Example bullet

Cut cross-report discrepancies 90% by sourcing one definition of truth.

Validation coverage

Share of reports and tables with checks.

Benchmark

Averagesome
Goodmost
Greatall

Measure with

Python Snowflake

Example bullet

Added tests on every key metric so a broken number paged us, not the CFO.

Time to trusted number

How long before a figure is sign-off ready.

Benchmark

Averagedays
Goodhours
Greatminutes

Measure with

BigQuery Snowflake

Example bullet

Took month-end numbers from a three-day scramble to same-day.

5

Efficiency & Automation

Slow, manual analysis does not scale. These show you reach the answer fast and hand the repetitive work to a script, not a person.

Query performance

How much faster your queries run.

Benchmark

Average-30%
Good-60%
Great-90%

Measure with

BigQuery Snowflake

Example bullet

Cut a core query from 40 seconds to under 3 with better joins and partitioning.

Time to insight

How fast you answer a new question.

Benchmark

Averagedays
Goodhours
Greatminutes

Measure with

BigQuery Python

Example bullet

Got ad-hoc questions answered in minutes with a reusable query library.

Manual work automated

Repetitive analysis you scripted away.

Benchmark

Average5 hrs/wk
Good20 hrs/wk
Greatan FTE

Measure with

Python pandas

Example bullet

Automated the weekly pull, saving 12 hours a week of copy-paste.

Reusable tables built

Shared tables and views you built for the team.

Benchmark

Averagea few
Gooddozens
Greatthe warehouse

Measure with

BigQuery Snowflake

Example bullet

Built 40 reusable tables the whole analytics team now queries off.

Cost of analysis

Warehouse spend you kept in check.

Benchmark

Average-15%
Good-40%
Great-60%

Measure with

Snowflake BigQuery

Example bullet

Cut warehouse spend 45% by killing runaway queries and materializing hot tables.

6

Reach & Stakeholder Adoption

Analysis only counts when someone uses it. These connect your work to the people, teams, and decisions it actually reached.

Stakeholders served

Teams or leaders relying on your analysis.

Benchmark

Average1 team
Goodseveral
Greatexec + org

Measure with

Tableau Looker

Example bullet

Became the analyst three product teams routed their data questions to.

Audience reached

People who see your reporting.

Benchmark

Average50
Good500
Great5k+

Measure with

Looker Power BI

Example bullet

Built the company KPI dashboard 900 people check weekly.

Data literacy raised

How much you upskilled the team on data.

Benchmark

Averagead hoc
Goodregular
Greata program

Measure with

Tableau Metabase

Example bullet

Ran the SQL training that got 40 non-analysts self-serving.

Reusable vs ad hoc

Share of work that became standing reporting.

Benchmark

Averagemostly ad hoc
Goodmixed
Greatmostly reusable

Measure with

Looker BigQuery

Example bullet

Turned one-off requests into the standing reports the org runs on.

Decision cadence

How regularly leaders use your numbers.

Benchmark

Averagequarterly
Goodmonthly
Greatweekly

Measure with

Tableau Power BI

Example bullet

Got the leadership team running its weekly review off my dashboard.

Do your best analysis numbers make the resume?

Data analysis throws off numbers most teams would love: revenue moved, churn cut, hours saved, adoption. The slip is tucking them under a parade of every tool you ever logged. Hard to spot these in your own draft.

That is my job.

I'll comb your Data Analyst resume like a hiring manager and mark which numbers to add, tighten, or drop. Free, inside 12 hours.

Get a Free Data Analyst Resume Review

I review personally all resumes within 12 hrs

PDF, DOC, or DOCX • under 5MB

Want to read more first? See how the resume review works →

Qualitative metrics

What if I don't have numbers to share?

No number does not mean no result. With nothing to point to, what you actually did and the decision it shifted still count. Each angle below gives a clean way to put it down, with one line ready to borrow.

1

Business & Decision Impact

Decision owned

When to use it: the decision was yours to shape

Example bullet

Owned the analysis that leadership leaned on to greenlight the launch.

Before / after direction

When to use it: the team acted but you never sized it

Example bullet

Turned a messy export into the read that changed how the team prioritized.

Problem framed

When to use it: you found the question worth asking

Example bullet

Reframed a vague ask into the analysis that exposed where the money leaked.

2

Dashboards & Reporting

Reporting owned

When to use it: the dashboards were yours to build

Example bullet

Owned the reporting layer the whole team now runs its week on.

Practice introduced

When to use it: you brought self-serve where there was none

Example bullet

Stood up the first self-serve dashboard the team stopped pinging analysts for.

Before / after direction

When to use it: reporting got faster but nothing recorded it

Example bullet

Replaced the weekly spreadsheet with a dashboard that updated itself.

3

Experimentation & A/B Testing

Practice introduced

When to use it: you brought real testing in

Example bullet

Stood up the experiment process the team now ships every change through.

Problem owned

When to use it: the bad test design was yours to catch

Example bullet

Caught the flawed test that would have shipped a losing change.

Before / after direction

When to use it: the test worked but you never sized the lift

Example bullet

Designed the experiment that settled a six-month debate with data.

4

Data Quality & Trust

Trust owned

When to use it: the credibility of the numbers was yours

Example bullet

Owned the cleanup that got leadership trusting the dashboard again.

Practice introduced

When to use it: you brought definitions where there were none

Example bullet

Set the metric definitions that ended the whose-number-is-right fights.

Before / after direction

When to use it: the numbers got cleaner but nothing tracked it

Example bullet

Rebuilt the reporting so two teams finally saw the same figure.

5

Efficiency & Automation

Automation owned

When to use it: the manual grind was yours to kill

Example bullet

Owned the automation that turned a two-day report into a button.

Before / after direction

When to use it: it got faster but nobody timed it

Example bullet

Rewrote the query stack so answers came back in seconds, not coffee breaks.

Practice introduced

When to use it: you set the reusable layer

Example bullet

Built the shared query library the team stopped reinventing.

6

Reach & Stakeholder Adoption

Outcome owned

When to use it: the adoption was yours to drive

Example bullet

Owned the reporting the leadership team now runs every weekly review on.

Before / after direction

When to use it: people used it but nobody counted them

Example bullet

Built the dashboard that quietly became the team's source of truth.

Ownership / scope

When to use it: you owned analytics for the team

Example bullet

Was the analyst the whole product org brought its numbers questions to.

Data analyst, or someone who just pulls numbers?

A long tool list does not show you change decisions; the figures do. Drop me the file and I'll show where it reads as real analysis and where it still amounts to a stack of pulled reports.

You get back a frank read of your data analyst resume with a short, blunt set of fixes, back to you within a day, no charge.

Get a Free Data Analyst Resume Review

I review personally all resumes within 12 hrs

PDF, DOC, or DOCX • under 5MB

Want to read more first? See how the resume review works →

Frequently asked

Data Analyst resume metrics FAQ

Go qualitative. The ideal is a number, but the scope you owned and the direction things moved still count. You can point to an analysis leadership acted on, a messy dataset you made trustworthy, or the dashboard the team now runs on. Recruiters read those as genuine analyst work, none of it invented. Each type above ships with a worked example.

Yes, provided it is a fair estimate you can defend. Say you cut a report's turnaround but never logged the original time: "near a third of what it once took" is reasonable. Use a relative figure when the exact values must stay private. All you owe is showing the way you reached that figure.

Do not. A data analyst interview gets into the work, and a fake number comes undone the second anyone asks how you sized the lift or what the baseline was. One fabricated number can end the whole loop. A line about what you owned stays honest and still counts.

No, only the strongest. Keep numbers for the two or three lines that carry the most inside the role you hold now, the first thing a reader sees. Put a number on all of them and the real ones get buried, and the page slides into filler. A few you can defend outweigh a screenful.

Whichever shows the result best. An impact figure reads well as an absolute ("$2.4M in revenue"); a change reads well in percent ("churn down 7 points"). Ditch a stray percentage that nothing supports. Pair them when possible: "cut reporting from 15 hours a week to under one."

Yes, and they come up more often than juniors expect. A report's turnaround before and after, the dashboard uptake you drove, an A/B test you read, or a data check you wrote are all reachable from one project or even an internship. A huge company is not required, just evidence your analysis got used.

More within reach than you would think. Adoption shows in your BI tool and product analytics; revenue and cost impact trace to the business figures your work fed; query and report times sit in the warehouse and your SQL editor; experiment results are in your testing tool. When it all happened a while back, estimate it carefully and call it an estimate.

Exactly one, up at the top. One headline figure, the revenue you shifted or your strongest adoption or efficiency win, buys the recruiter's next few seconds. Leave the rest for the work-experience bullets. The data analyst resume guide covers writing that summary.

Who wrote this

Built by an ex-Google recruiter

Emmanuel Gendre, former Google Recruiter and Tech Resume Writer

Emmanuel Gendre

Former Google recruiter · 12 years · 1,500+ tech resumes rewritten

I screen Data Analyst resumes the same way I did at Google: against the role profile, against the JD, and against the bar real hiring managers set. The metrics on this page are the ones I tell my own clients to chase.

Read my full story →

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