Data Analyst Resume Example

The lead bullet is a finding, not a deliverable

"Customers who never connected a second data source churned 3x faster" is the observation, the onboarding redesign is what changed, and the 12% lift in 90-day retention is what happened afterwards. That chain is the whole job of an analyst, compressed into one line.

Scale is measured in people, not in queries

The dbt and Looker metrics layer is described as used daily by 40 employees across product, sales, and finance. That tells a reviewer the models survived contact with three departments, which is a harder claim than the number of tables built.

Soft value gets a number anyway

Automated reporting is easy to wave at. Roughly 10 analyst-hours a week is a figure a hiring manager can convert into headcount, and it is honest about being an estimate rather than pretending to a precision nobody measured.

The rigor habit is stated as a habit

Checking nulls, duplicates, and schema drift as the first step of every analysis says more about reliability than the word detail-oriented ever will. It also gives an interviewer a specific thing to ask about, which is usually a conversation you want.

An economics degree is carried by the bullets

There is no statistics or computer science degree here, and the resume does not apologize for it. The B.A. sits at the bottom while four years of SQL, Python, and shipped analysis do the persuading above it.

Match the headline to the posting's job title

Data Analyst, Business Analyst, Product Analyst, and Analytics Engineer are four different searches even when the work overlaps. Use the words in the posting rather than the words your last company happened to use in your offer letter.

Reorder the skill groups around their stack

The three groups stay. If the posting runs on Power BI and Snowflake rather than Looker and dbt, put what you actually know of their stack first in each line and drop the tools you would not want to be quizzed on.

Compress the junior role as your history grows

The Atlas Research role is already down to two bullets while the current role gets four. Keep that taper: the survey cleaning work earns its place only because it shows Python and volume, not because it was recent.

Replace the percentages with your own, honestly

If nobody measured the retention effect of your analysis, say what you found and what was changed because of it. "Identified the drop-off point that drove the checkout rework" is weaker than a number and stronger than a figure you invented.

Counting dashboards instead of decisions

"Built 30 dashboards" tells a reviewer how busy you were. It says nothing about whether anyone opened them. The stronger version names one dashboard and the decision it changed, which is what this example does with the metrics layer.

A percentage with no denominator behind it

"Improved conversion by 40%" could mean a move from 1% to 1.4% on 200 sessions. Give the base, the population, or the absolute figure, because an analyst who hides the denominator invites the suspicion that it is embarrassing.

A tool inventory with no depth anywhere

Twelve BI tools on one line reads as exposure, not skill. Reviewers of analyst resumes discount long tool lists by habit, so keep the four or five you use weekly and let the bullets show what you did with them.

Leaving SQL as a single undifferentiated word

Everyone writes SQL on an analyst resume. What separates candidates is whether the bullets imply window functions, incremental models, and query tuning, or whether they imply pulling a table someone else already modeled for you.