Data Analyst Resume Example

Entry levelGraded B by our roast

An entry-level data analyst two years into a first job at a meal-kit subscription company, with SQL, dbt and Tableau and a summer analytics internship behind it.

Rowan Okafor
Data Analyst | SQL, dbt, Tableau | Subscription Retail
Columbus, OH · (555) 019-1103 · rowan.okafor@example.com
Profile

1Data analyst, two years at a 40,000-subscriber meal-kit company. Writes SQL in Snowflake and dbt and builds the Tableau views the growth team runs standup from.

Experience
Data Analyst, Harvest CrateJun 2024 - Present
  • 2Found one skipped-week email drove 31% of week-two cancels. The rewrite lifted 8-week retention from 54% to 58%
  • 3Built the weekly retention dashboard in Tableau (LOD calcs) that 14 growth and CX staff open every Monday
  • 4Ruled out price as the cause of a 6-point Midwest churn gap, which pointed the team to late deliveries instead
  • Modeled 22 dbt tables from 6 sources (Stripe, Shopify, Braze), cutting the daily refresh from 3 hours to 25 min
  • 5Built a cohort LTV model in Python (pandas) that finance now uses to cap the first-box discount at $40
Analytics Intern, Scioto Valley Credit UnionMay 2023 - Aug 2023
  • Wrote SQL (CTEs, window functions) to flag 1,900 dormant accounts, and 240 reactivated after a mail campaign
  • Replaced a 40-tab Excel branch report with one Power BI page, saving the ops manager about 6 hours a month
Education
B.S. Data Analytics, The Ohio State University, 2024
Tableau Desktop Specialist, 2025
Skills
SQL (CTEs, window functions), Snowflake, dbt, Tableau (LOD calcs), Python (pandas), Power BI, A/B test analysis

A fictional resume written by our team. The person, employers and numbers are invented; the tools, licenses and schools are real.

Why it works

  1. 1

    “Data analyst, two years at a 40,000-subscriber meal-kit company. Writes SQL in Snowflake and dbt and builds the Tableau views the growth team runs standup from.”

    Names the business, the warehouse and who uses the work in two sentences. Those are the first three things an analytics manager wants to know.

  2. 2

    “Found one skipped-week email drove 31% of week-two cancels. The rewrite lifted 8-week retention from 54% to 58%”

    Question, finding, decision and the metric before and after, all in one line. That chain is what separates analysis from reporting.

  3. 3

    “Built the weekly retention dashboard in Tableau (LOD calcs) that 14 growth and CX staff open every Monday”

    Says who opens it and how often. Adoption answers the dashboards-nobody-uses doubt before the reader raises it.

  4. 4

    “Ruled out price as the cause of a 6-point Midwest churn gap, which pointed the team to late deliveries instead”

    A negative result written as a decision. Ruling something out with data is real work and shows judgment.

  5. 5

    “Built a cohort LTV model in Python (pandas) that finance now uses to cap the first-box discount at $40”

    Ends on the decision the model drives. Naming the team that uses it turns a technical task into business impact.

How it scored

B
Roast grade
8
Buzzword score out of 100, lower is better

We ran this example through the same free roast you can use, three times, and kept the middle result. The roast ranks a resume against a typical applicant pile and grades hard: even a resume with a number in nearly every bullet usually lands in the B range.

Copy the text

Use it as a skeleton, then replace every line with your own work. A copied bullet with someone else's numbers is the fastest way to fail an interview question.

Plain text, ready to edit
Rowan Okafor
Data Analyst  |  SQL, dbt, Tableau  |  Subscription Retail
Columbus, OH | rowan.okafor@example.com

SUMMARY
Data analyst, two years at a 40,000-subscriber meal-kit company. Writes SQL in Snowflake and dbt and builds the Tableau views the growth team runs standup from.

EXPERIENCE
Data Analyst, Harvest Crate | Jun 2024 - Present
- Found one skipped-week email drove 31% of week-two cancels. The rewrite lifted 8-week retention from 54% to 58%
- Built the weekly retention dashboard in Tableau (LOD calcs) that 14 growth and CX staff open every Monday
- Ruled out price as the cause of a 6-point Midwest churn gap, which pointed the team to late deliveries instead
- Modeled 22 dbt tables from 6 sources (Stripe, Shopify, Braze), cutting the daily refresh from 3 hours to 25 min
- Built a cohort LTV model in Python (pandas) that finance now uses to cap the first-box discount at $40

Analytics Intern, Scioto Valley Credit Union | May 2023 - Aug 2023
- Wrote SQL (CTEs, window functions) to flag 1,900 dormant accounts, and 240 reactivated after a mail campaign
- Replaced a 40-tab Excel branch report with one Power BI page, saving the ops manager about 6 hours a month

EDUCATION AND CREDENTIALS
B.S. Data Analytics, The Ohio State University, 2024
Tableau Desktop Specialist, 2025

SKILLS
SQL (CTEs, window functions), Snowflake, dbt, Tableau (LOD calcs), Python (pandas), Power BI, A/B test analysis

How does yours compare?

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More graded examples

Avery Lindqvist
Senior Data Scientist, Forecasting and Pricing
Chicago, IL · (555) 017-9169 · avery.lindqvist@example.com
Summary

Senior data scientist, 8 years, building demand and pricing models that run in production and feed weekly decisions. Python, SQL and Spark. Owns monitoring and retraining, not just the notebook.

Experience
Senior Data Scientist, Harlow Grocery GroupMay 2022 - Present
  • Built a store-level demand forecast in LightGBM that cut WAPE from 34% to 21% against the old moving-average baseline
  • Shipped it as a nightly Airflow batch for 310 stores, trimming perishable waste $4.1M a year per a finance audit
  • Added drift monitoring and monthly retraining, which flagged a holiday demand shift 9 days before it hit orders
  • Designed markdown pricing tests across 48 stores with CUPED and power analysis, lifting gross margin 1.8 points
Data Scientist, Crestline Mutual InsuranceAug 2018 - Apr 2022
  • Built a claims triage model with 0.81 recall at 0.60 precision, up from 0.52 recall under the rules-based system
  • Served it through a FastAPI endpoint to 140 adjusters, cutting median time to assign complex claims from 6 days to 2
  • Rebuilt the feature pipeline in Spark, dropping the weekly refresh from 11 hours to 50 minutes
Education
M.S. Statistics, University of Illinois Urbana-Champaign, 2018
B.S. Mathematics, Northwestern University, 2016
Skills
Python, SQL, Spark, LightGBM, Airflow, dbt, Experiment design, Causal inference
Data Scientist
Graded B+
Jordan Okafor
Senior Software Engineer, Payments and Platform
Seattle, WA · (555) 015-3305 · jordan.okafor@example.com
Profile

Backend engineer, 9 years in Go and Python, owning payment and order services at 18k requests per second. Known for migrations that ship without incidents and for cutting cloud spend without cutting reliability.

Experience
Senior Software Engineer, Larkspur CommerceApr 2021 - Present
  • Rebuilt the checkout pricing service in Go, cutting p95 latency from 820ms to 190ms at 18k requests per second
  • Led the move of 64 services from self-hosted Kafka to Amazon MSK over 5 months with zero customer-facing incidents
  • Cut monthly AWS spend from $212k to $151k by rightsizing RDS and moving batch jobs to Spot, with no SLO breaches
  • Wrote the on-call runbook and postmortem template, dropping mean time to recovery from 74 to 22 minutes in a year
Software Engineer II, Brightwater LogisticsJun 2017 - Mar 2021
  • Built the carrier rate API in Python and PostgreSQL that quoted 2.3M shipments a month for 1,400 merchants
  • Replaced a 6-hour nightly reconciliation job with an event-driven pipeline that settles each order in under 4 minutes
  • Cut the flaky test rate in CI from 11% to under 1%, bringing median deploy time from 38 to 12 minutes
  • Mentored 4 junior engineers, 3 of whom were promoted within 18 months, and ran the weekly design review
Education
B.S. Computer Science, University of Washington, 2017
AWS Certified Solutions Architect, Professional, 2023
Skills
Go, Python, PostgreSQL, Kafka, AWS (ECS, RDS, MSK), Terraform, Kubernetes, Datadog
Software Engineer
Graded A
Riley Castellano
Product Manager, B2B SaaS Onboarding
Austin, TX · (555) 015-4165 · riley.castellano@example.com
Profile

Product manager on a B2B scheduling app used by 9,000 clinics. Owns activation and early retention, writes own SQL, and ships 2 to 3 experiments a month with 7 engineers and a designer.

Experience
Product Manager, Tidewell Health SoftwareFeb 2023 - Present
  • Cut onboarding from 9 steps to 4 after recordings showed 58% drop-off at calendar sync, raising activation 31% to 47%
  • Dropped a requested reporting dashboard after 14 interviews showed 3 of 40 accounts would use it, freeing 2 engineers
  • Found in SQL that clinics booking 20+ visits in week 1 retain at 2x the rate, and made it the team's north star metric
  • Shipped SMS reminders behind a holdout test. No-shows fell 23% and 90-day logo churn dropped from 6.1% to 4.4%
Associate Product Manager, Tidewell Health SoftwareJun 2021 - Jan 2023
  • Redesigned 3 billing failure states, cutting billing support tickets from 410 to 160 a month
  • Ran a pricing page A/B test across 22k visitors that lifted trial starts 14% with no drop in paid conversion
Customer Success Manager, Northgate SchedulingJul 2019 - May 2021
  • Tagged 1,200 support tickets into a quarterly report that set 4 of the next year's 10 roadmap items
Education
B.A. Economics, University of Texas at Austin, 2019
Reforge, Product Strategy program, 2024
Skills
SQL, Amplitude, Experiment design, Customer interviews, Figma, Looker, Pricing tests
Product Manager
Graded B+