A data analyst's career description should show what decisions your analyses changed. For each project, state the business question, the data and tools, your role and the measurable result, such as churn reduced, time saved or revenue influenced. A compact layout keeps the focus on outcomes rather than on every query you wrote.
For: Data analysts with a few years of experience who want to show business impact, not only queries
Start with this example ↗All people, companies and numbers here are fictional. Replace them with your own career.
Gayoung Jeon
Data Analyst · 5 years
gayoung.jeon@example.com
Core strengths
• Cut monthly churn from 5.8% to 5.1% with a model-driven campaign
• Replaced 11 manual reports with 3 self-serve dashboards
• Reviews 24 A/B tests a quarter for sample size and validity
• Writes production SQL and Python for pipelines of 50M+ rows
Career details
Example Subscription Media · Data Analytics Team Data Analyst
2022-02 – Present
Responsibilities Analyzes subscriber behavior and supports product and marketing decisions with dashboards and experiments.
Churn Early-warning Model
2023-04 – 2023-10
Role Lead analyst · Team Team of 4 · Contribution 60%
Technologies Python · BigQuery · scikit-learn
• Built a churn model on 18 months of usage logs and found 3 early signals.
• Supported a retention campaign that cut monthly churn from 5.8% to 5.1%.
• Scored 100% of active subscribers weekly through an automated job.
Experiment Review Process
2022-06 – 2022-12
Role Process owner · Team Team of 3 · Contribution Lead
Technologies SQL · Python · Confluence
• Created a review checklist for about 24 A/B tests per quarter.
• Stopped 5 underpowered tests before launch through sample-size checks.
• Cut analysis turnaround per test from 5 days to 2.
Example Retail Group · Business Intelligence Team Junior Data Analyst
2019-07 – 2022-01
Responsibilities Built sales reporting and ad hoc analyses for store operations and merchandising.
Promotion Effectiveness Analysis
2021-02 – 2021-08
Role Analyst · Team Team of 3 · Contribution 40%
Technologies SQL · Python · Excel
• Measured incremental sales for 30 promotions using matched store groups.
• Showed 9 promotions lowered margin, and those were dropped the next season.
Sales Reporting Automation
2020-03 – 2020-11
Role Analyst · Team Team of 2 · Contribution 70%
Technologies SQL · Tableau · Airflow
• Replaced 11 manual Excel reports with 3 Tableau dashboards.
• Saved the sales team about 14 hours of reporting work per week.
• Reached 85 weekly active dashboard users across 40 stores.
Reason for leaving Moved to a product-focused analytics role
Data tools BigQuery · Airflow · Tableau · Looker Studio
Certifications
Advanced Data Analytics Semi-Professional (ADsP) · Korea Data Agency 2020-05
SQL Developer (SQLD) · Korea Data Agency 2021-03
Education
Example University
2015-03 – 2019-02
Statistics · B.S. · Graduated
Three keys to a Data Analyst career description
01
Start from the business question
Each project should open with the question someone needed answered, such as why repeat purchases dropped in one segment. Then describe the analysis and the decision it supported. Reviewers look for analysts who connect data to action, not only to charts.
02
Separate the analysis result from the business result
'Found that 3 factors predict churn' is an analysis result; 'retention campaign based on those factors cut churn by 12%' is a business result. Write both when possible, and be honest about which parts were yours and which the marketing or product team executed.
03
Name tools per project, briefly
List the 2 to 5 tools used in each project, such as SQL, Python and Tableau, and keep the full list in the skills section. This shows where you applied each tool and avoids a long, unfocused stack at the top of the document.
Rewrite result lines like this
BeforeCreated various dashboards for the business team.
AfterReplaced 11 manual Excel reports with 3 Tableau dashboards, saving the sales team about 14 hours per week.
Counting what was replaced and the time saved turns a deliverable into a measurable result.
BeforeWe analyzed customer data to reduce churn.
AfterBuilt a churn model on 18 months of usage logs that identified 3 early signals; the retention campaign built on it cut monthly churn from 5.8% to 5.1%.
Your analysis and the team's execution are both named, so the reader sees your part and the business effect.
BeforePerformed A/B test analysis.
AfterSet up a test review process for 24 experiments a quarter, including sample-size checks that stopped 5 underpowered tests before launch.
A process with counts shows analytical rigor better than naming a task type.
Common mistakes
Listing queries and dashboards without the decision they supported
Claiming business results that depended mostly on other teams
Stacking 15 tools at the top instead of naming them per project
Quoting internal revenue or user numbers that should be expressed as ratios
Frequently asked questions
What if my analysis did not lead to a clear business result?
Describe the decision it informed, even if the decision was to not launch something. 'Showed that a planned discount would lower margin by 9%, and the plan was dropped' is a valid result. Avoid inventing downstream impact you cannot support.
Should I include code or notebooks?
Not in the document itself. A link to a public repository with sample analyses is useful if it uses public or synthetic data. Never share notebooks containing company data, even partially.
How do I show statistical skill without jargon?
Describe the method briefly and its purpose, such as 'logistic regression to rank churn risk' or 'sample-size checks for A/B tests'. Keep the emphasis on what the method made possible and save deeper detail for the technical interview.
Build your career description from this example
The button opens a career description filled with this example in the editor. You need to sign in, and you can change content and design freely. The editor's check panel flags result lines without numbers and the page count.