Data analyst with 4 years of experience in retail and logistics, turning messy operational data into dashboards and analyses that drive weekly decisions. Built a delivery performance model that cut late shipments 17%. Fluent in SQL, Tableau and Python, with a knack for explaining findings to non-technical managers.
Experience
Data Analyst, Redline Freight Partners (Dallas, TX)
Apr 2023 – Present
Analyzed 2M shipment records to identify carrier and lane delays, informing routing changes that cut late deliveries 17%
Built Tableau dashboards for 60 operations managers covering on-time rate, cost per mile and claims
Automated weekly KPI reporting with SQL and Python, saving 10 analyst hours per week
Partnered with finance to model fuel surcharge scenarios, supporting $1.2M in contract renegotiations
Junior Data Analyst, Pinecrest Home Goods (Fort Worth, TX)
Jan 2022 – Mar 2023
Cleaned and reconciled point-of-sale data from 85 stores, raising report accuracy to 99%
Built a markdown analysis that reduced end-of-season clearance losses 8%
Built a weekly store traffic report used by 10 district managers to plan staffing
Data Analytics Intern, Brazos Valley Food Bank (Bryan, TX)
Jun 2021 – Aug 2021
Mapped food distribution against county need data, informing 2 new mobile pantry routes
Education
B.S. in Statistics, Texas A&M University (Aug 2017 – Dec 2021)
Data: Snowflake, PostgreSQL, data cleaning and validation
Certifications: Tableau Desktop Specialist
How to write a Data Analyst resume
A strong Data Analyst resume shows the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Put your most relevant recent role first and make every bullet show a result, not a duty.
Sections, in order
Summary
Experience
Projects
Skills
Education
Length: One page for most candidates; move to two only if every line is relevant.
Summary: Write two or three lines: your role, your strongest skills, and one result that proves them.
Skills to feature
SQL
Python or R
Statistics
Machine learning
Data modeling
dbt and ELT pipelines
BI tools (Looker, Tableau, Power BI)
Experiment design
Data visualization
Stakeholder communication
Example bullet points
Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year
Rebuilt the revenue dashboard on dbt and Looker, cutting refresh time from 6 hours to 20 minutes
Designed 25 A/B tests with sequential analysis, shortening the average test from 4 weeks to 10 days
Tips from recruiters
Pair every technique with the decision it informed; "built a model" alone says little.
List tools under grouped skills ("Analytics: SQL, dbt, Looker") so ATS keyword matches are exact.
Link a notebook, Kaggle profile or public analysis if you have one.
Recommended templates
Classic (ATS-friendly): Jake's-style LaTeX classic: small caps and hairline rules
Modern (ATS-friendly): Clean single column with accent headings and rules
Compact (ATS-friendly): Dense single column that fits long histories
Data Analyst resume FAQ
What should a Data Analyst resume include?
A Data Analyst resume should show the questions you answered, the models or pipelines you shipped, and the decisions your analysis changed. Use these sections in this order: Summary, Experience, Projects, Skills and Education. Put your most relevant recent role first and make every bullet show a result, not a duty.
What is the best resume template for a Data Analyst?
Classic, Modern and Compact work well for Data Analyst roles. They are single-column with standard headings, so applicant tracking systems (ATS) parse them reliably.
How long should a Data Analyst resume be?
One page for most candidates; move to two only if every line is relevant.
Which skills should a Data Analyst put on a resume?
Prioritize the skills named in the job posting. Common ones for Data Analyst roles are SQL, Python or R, Statistics, Machine learning, Data modeling and dbt and ELT pipelines.
How do I write strong Data Analyst resume bullet points?
Start with an action verb, say what you did, and end with a measurable result such as forecast accuracy, revenue impact and query time. For example: "Built a churn model (gradient boosting, AUC 0.86) that targeted retention offers and saved $900k a year."