Resume template · Updated October 2026
Data quality analyst resume template
Short answer
Meridian suits a data quality analyst: skills print above experience, so SQL, profiling and the quality tools you use are seen first, and the jobs then show the issues you found and fixed. The fictional sample checks customer and order data at an online retailer and traced a duplicate customer problem back to a signup form. Show root causes fixed, not only errors counted.
Download the data quality analyst sample
Tomas is a fictional data quality analyst. Replace every detail with your own before you send it.
The files are made in your browser by Resumity's own exporters, from this sample. The builder opens it as your own resume: click any line to replace it. Free plan, no watermark.
Why Meridian suits a data quality analyst resume
Meridian prints a summary, labelled skills rows and then experience, in black and grey with a centred header. Data quality postings screen for SQL, profiling and testing tools, so placing skills above the jobs matches the first check a hiring manager makes.
Second choice: FAANGPath. FAANGPath keeps the same skills-first order with full-width rules, a fit if you are applying to a data engineering team.
What goes in each section, in Meridian's order
Meridian prints its sections in a fixed order, the same in the PDF and the Word file. Here is that order for this sample, and what to put in each section.
- Professional Summary. The data you check (customers, orders, claims, products), the rules and tools you run, and one result such as defects found before reports went out.
- Skills. Rows for SQL and profiling, quality and testing tools, and the platforms you work in. Keep each row honest and short.
- Work Experience. Bullets that go from finding to fix: the rule you wrote, the defect it caught, the root cause, and who changed what upstream.
- Education. Degree with the year. Statistics, information systems or business analytics fit well.
The sample resume as text
The same content as the files above, so you can copy a line or compare it with your own.
Tomas Herrera
Data Quality Analyst, E-commerce
Phoenix, AZ | tomas.herrera@example.com | (602) 555-0149 | linkedin.com/in/sample-tomas-herrera
Data quality analyst checking customer, order and product data for an online retailer. Runs about 260 automated quality rules and works with engineering to fix defects at the source.
Experience
Data Quality Analyst, Saguaro Home Goods Online, Phoenix, AZ (10/2022 to Present)
- Wrote and maintain about 260 quality rules in dbt tests and Great Expectations across customer, order and product tables.
- Traced a rise in duplicate customer records to a guest checkout form that skipped email matching; after the fix, new duplicates fell by most of their previous rate.
- Built a quality dashboard for finance and marketing that shows which tables passed checks that morning.
- Profile each new supplier product feed before it goes live and send the supplier a list of missing fields.
Business Analyst, Copper State Distributors, Tempe, AZ (07/2019 to 09/2022)
- Reconciled weekly sales reports against the ERP and found two broken product mappings.
- Cleaned and merged customer lists from three regional systems before a CRM move.
- Documented each report's data sources for the finance team.
Education
Bachelor of Science, Business Analytics, Arizona State University (05/2019)
Skills
SQL, Data profiling, Root cause analysis, Deduplication, Validation rules, dbt, Great Expectations, Snowflake, Looker, Writing clear defect reports
How to make the sample yours
Follow one issue to its source
A bullet that traces bad data back to a form, a feed or a mapping, and shows the fix upstream, proves more than a count of errors flagged.
Count rules and coverage
Quality rules in production, tables covered, checks run per day. Coverage shows the scale of your work.
Show who trusts the data now
Reports reissued less often, fewer questions from finance, a dashboard that stopped being double-checked. Trust is the real product of data quality work.
Keep personal data out
Describe defects by type, such as duplicate customers or missing postal codes, never with real records.
Keywords to keep where they are true
Terms this kind of resume is usually searched for. Use the posting's exact wording, and only for work you have done.
- data quality
- data profiling
- data validation
- SQL
- root cause analysis
- data cleansing
- deduplication
- quality rules
- Great Expectations
- dbt tests
- data observability
- master data
Mistakes to avoid
- Counting errors without saying what was fixed.
- Real customer records as examples.
- Tool names you have not used in production.
- A summary that reads like a data analyst's.
How the sample PDF reads in text extractors
We exported this sample with Resumity's PDF exporter in Chrome on 2026-10-29 and read the file back with three open-source text extractors. This shows whether software that turns a PDF into text can recover it; it is not a test of any commercial applicant tracking system. How we test templates.
| Extractor | Name, email, phone | Headings | Bullets intact |
|---|---|---|---|
| pdf.js 4.10.38 | All found | 4 of 4 | 7 of 7 |
| pdftotext (Xpdf) 4.00 | All found | 4 of 4 | 7 of 7 |
| pypdf 6.13.0 | All found | 4 of 4 | 7 of 7 |
Frequently asked questions
What should a data quality analyst put in the summary?
The data domains you check, the number of rules or tables you cover, and one result such as defects caught before reports were published.
Is data quality work considered engineering or analysis?
It sits between the two. Show SQL and testing skills for engineering teams, and business impact for analytics teams.
How do I show impact if I only flagged issues?
Describe what happened next: who fixed the cause, what changed in the process, and how the data looked afterwards. If nothing changed, say what you recommended.
Should I include data quality dimensions such as completeness?
Use them inside bullets to describe the checks you ran. A list of dimension names on its own adds little.
Will applicant tracking systems read this Meridian PDF?
The sample PDF read back with its contact details, all 4 section headings and all 7 bullets intact in three open-source text extractors (pdf.js, pdftotext and pypdf). Commercial systems such as Workday, Greenhouse or Taleo were not tested, and each employer configures its own.
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