Data Analyst Resume Template
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Analyst with five years turning warehouse data into revenue decisions at marketplace and fintech companies. Modelled 180M rows in SQL and dbt, cut reporting turnaround 70%, and drove pricing changes worth $2.4M.
- Own the pricing analytics model end-to-end, informing $2.4M in annual margin decisions with 4 stakeholder teams each quarter.
- Design dbt models over 180M order rows against a 6am freshness SLA, cutting the finance close from 9 days to 2.
- Automate 23 recurring Looker reports, returning 31 hours per week to a 6-person analytics team.
- Built a churn model in Python flagging 12K at-risk accounts per month, driving a retention campaign worth $780K.
- Replaced 40 hand-maintained spreadsheets with a governed Snowflake layer, removing 6 recurring reconciliation errors.
- Partnered with marketing to attribute 14 acquisition channels, reallocating $1.1M of spend and lifting ROAS 27%.
- Developed 60 Tableau dashboards used weekly by 240 staff across 3 business units.
- Reduced warehouse query cost 44% by refactoring 18 nightly jobs, scaling to 3x data volume with no added spend.
- Delivered a demand forecast at 8% MAPE, cutting stockouts 22% across 90 SKUs.
Analyst resumes fail for a specific reason: they describe tools instead of decisions. Listing SQL, Tableau and Python tells a hiring manager what you touched, not what changed because you touched it. The template below is written the other way round, and the score on this page is the actual output of the proof engine, not a marketing number.
Why most data analysts don't get the call
The decision, not the dashboard
"Built 40 dashboards" is a workload. "Built the pricing model behind $2.4M of margin decisions" is an outcome. Analysts get filtered out at the first skim because their bullets end at the artefact. Push every bullet one step further: who acted on it, and what moved.
Row counts and refresh cadence are your scale numbers
Engineers have latency and QPS. Analysts have rows modelled, tables owned, reports served, and how fast the data lands. "180M order rows" and "a 6am freshness SLA" tell a reviewer what size of environment you can survive in.
SQL depth gets probed within ten minutes
Window functions, CTEs, incremental models and query cost show up in nearly every analyst screen. If you have tuned a warehouse bill or rewritten a nightly job, that belongs on the page — it separates you from someone who only ever wrote SELECT statements in a BI tool.
Say which methods, not "statistical analysis"
Keyword filters match literal terms. A/B testing, cohort analysis, regression, forecasting and attribution are searchable. "Strong analytical skills" matches nothing and signals nothing.
Use the pattern, not the fictional story.
Keep the hierarchy
Preserve the clear section order and plain-text contact details.
Replace every claim
Swap in your own scope, metrics, tools, and outcomes—never borrowed achievements.
Run the proof again
Open Studio and watch the score respond to the evidence in your version.
What the keyword filter is looking for
Applicant tracking systems match on literal strings. Spell each term the way the job posting spells it and group related skills so people and parsers can recover the context.
Why each choice was made
Why the skills section is split four ways
A single block of twenty tools reads as noise and trips the engine's ungrouped-skills rule. Four labelled groups let a keyword filter match the string and a human skim the shape of your stack in about two seconds.
Why dollar figures appear next to technical work
Analyst work is easy to describe as internal plumbing. Attaching the business number — $780K retention campaign, $1.1M reallocated spend — is what moves a bullet from support function to revenue function in a reviewer's head.
Where to put a portfolio
One link in the header, next to LinkedIn. A GitHub with three real notebooks beats a Projects section that repeats what your job already proves.
Adapt this template to your own experience.
Open the studio with this resume pre-loaded. Edit any bullet and watch the proof engine re-score it in real time. No account, no upload — your data stays in the browser.
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What each system will do to this file
Accepts PDF, DOC, DOCX, RTF, TXT · parses up to 2.5 MB.
Accepts PDF, DOC, DOCX, RTF, TXT, HTML, ODT, WPD · up to 5 MB · Varies by employer engine.
Accepts PDF, DOC, DOCX, RTF, JPG, PNG · up to 2 MB · Textkernel engine.
Accepts PDF, DOC, DOCX · up to 50 MB.
Accepts PDF, DOC, DOCX, RTF, HTML, ODT · up to 5 MB.
Accepts PDF, DOC, DOCX, RTF · Manatal (in-house) engine.