Data quality analyst resume examples & writing guide for 2026

Three data quality analyst resumes following one analyst from mortgage loan data rules to leading a bank's data quality program, rendered in a real template and annotated line by line. Each counts what a governance lead checks: rules, issues traced, controls and findings.

By the Resumi editorial teamUpdated October 5, 20268 min read

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Data quality analyst resume example, mid level, rendered in The Jake's template

Written by the Resumi editorial team for a fictional candidate. Role facts come from O*NET and the U.S. Bureau of Labor Statistics.

Strength band is Resumi’s optimization score, not a ranking any employer sees.

What makes this resume work

Each marker on the page has a note here. Press a marker to jump to its note. Hover a note and its marker lifts.

1
Data, tools and platform named
Data quality analyst, regulatory and risk data, SQL, dbt and Collibra. The headline names the regulated data and the governance platform that bank postings ask for this year.
2
Rules tied to critical data
Fourteen hundred rules in Collibra for 260 critical data elements behind regulatory reports. Bank postings talk about critical data and critical reports, and this ties the rule count directly to them.
3
Report adjustments cut upstream
Late call report adjustments cut 87%, from 31 a quarter to four, with checks added upstream. A regulator sees adjustments, so fewer of them is the result a bank's finance leaders notice.
4
Tests that run before reports
Three hundred twenty dbt tests and freshness checks catching 95% of failures before morning reports ran. Testing data before anyone reads it is the gap analytics surveys say teams still leave open.
5
Fixed at the source, not patched
Fifty-eight issues traced to source systems with lineage maps, 71% fixed where they started. Recruiters say interviews probe how you trace issues to their source, and this answers with a share.
6
Control evidence with no findings
Evidence for two regulatory exams and three internal audits, with no findings. Bank postings ask data quality staff to support regulatory responses and audits, and the outcome is what counts.

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01The example, in full

The resume shown above, as text you can search and copy.

Cordelia Nakashima

Data quality analyst, regulatory and risk data, SQL, dbt and Collibra

Pittsburgh, PAcordelia.nakashima@example.com(555) 473-1196

Summary

Data quality analyst with six years in financial data, the last four at a regional bank. Runs 1,400 data quality rules over regulatory and risk data, cut late call report adjustments 87%, and moved monitoring into dbt tests that catch failures before reports run. CDMP Practitioner.

Experience

Data quality analyst, regulatory and risk data

Aug 2022 – Present

Allegheny Commonwealth Bank · Pittsburgh, PA

Implemented 1,400 data quality rules in Collibra for 260 critical data elements behind regulatory reports.
Reduced late call report adjustments 87%, from 31 a quarter to 4, by adding completeness and reconciliation checks upstream.
Built 320 dbt tests and freshness checks on the Snowflake warehouse, catching 95% of failures before morning reports ran.
Resolved 58 data issues by tracing them to source systems with lineage maps, with 71% fixed at the source rather than patched.
Delivered control evidence for 2 regulatory exams and 3 internal audits covering 260 critical data elements, with 0 findings.

Data quality analyst

Jun 2020 – Jul 2022

Monongahela Mortgage Servicing · Canonsburg, PA

Built a borrower master record in SQL that merged 5,800 duplicates across 2 servicing systems.

Education

Bachelor of Science in Business Analytics

Aug 2016 – May 2020

Mon Valley State University · Pittsburgh, PA

Skills

Practice: Data quality, Data quality rules, Data issue management, Root cause analysis
Governance: Data lineage, Metadata management, Regulatory reporting, Data observability
Tools: Collibra, dbt, Snowflake, SQL

Certifications

Certified Data Management Professional (CDMP), Practitioner

Nov 2024

DAMA International

02Market snapshot

Median pay: $120,230 median annual pay for data scientists, the closest occupation BLS reports
Typical education: Bachelor's degree, the typical entry education for data scientists
Job outlook: +35% for data scientists, 2025–35, about 95,400 more jobs
Common credentials: 17 certifications listed for data scientists, none of them for data quality

Sources: BLS OES (May 2025) · BLS OOH (2025) · CareerOneStop (2026)

03The same resume at three levels

The tinted row is the level shown above.

Entry-level
0–2 years
The degree leads, then two years of loan data work in four counted bullets: rules written with the dimensions they cover, duplicates cut by profiling, issues closed with root causes, and a scorecard automated. The internship keeps one line on the glossary it started.
Typical titles: Data quality analyst I, Data integrity analyst, Junior data quality analyst
Mid-level
3–6 years
The default view. Six years in the units a governance lead weighs: rules tied to critical data elements, report adjustments cut, warehouse tests that catch failures early, issues fixed at the source, and control evidence with no findings. The servicing role keeps one line, and CDMP sits beneath.
Typical titles: Data quality analyst, Data governance analyst, Data integrity analyst
Senior
7+ years
Leading the program replaces writing rules: four analysts and thirty stewards, an enterprise standard with scorecards, three years without restatements, quality gates on model data, and exam evidence with no findings. The two earlier roles keep one line each, and CDMP Master sits beneath.
Typical titles: Data quality lead, Data quality manager, Data governance lead

04Summary

Weak

Detail-oriented data quality analyst passionate about ensuring data accuracy and integrity, with strong SQL skills and experience in data governance.

Strong

Data quality analyst with six years in financial data, the last four at a regional bank. Runs 1,400 data quality rules over regulatory and risk data and cut late call report adjustments 87%.

A data governance lead reads for the data you protect, how many controls you run and what improved because of them. The strong version names regulatory and risk data, a rule count and a reporting result. The weak version names accuracy and integrity, which every candidate claims.

05Bullets

Weak

Monitored data quality metrics and worked with teams to fix data issues and improve accuracy across systems.

Strong

Resolved 58 data issues by tracing them to source systems with lineage maps, with 71% fixed at the source rather than patched.

Cleaning and manipulating raw data is a core data scientist task, but data quality roles are judged on whether problems stop coming back. The strong bullet counts the issues, names how they were traced and what share were fixed where they started. The weak bullet describes monitoring, which a reviewer cannot check.

06Skills and keywords

Data quality practice

Data quality (in the example)
Data quality rules (in the example)
Data profiling
Data issue management (in the example)
Root cause analysis (in the example)
Data observability (in the example)
Master data management

Governance and regulation

Data governance
Data lineage (in the example)
Metadata management (in the example)
Regulatory reporting (in the example)
BCBS 239

Tools and credentials

SQL (in the example)
Python
dbt (in the example)
Snowflake (in the example)
Collibra (in the example)
Informatica Data Quality
Great Expectations
Certified Data Management Professional (CDMP) (in the example)

Filled chips appear in the example above.

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07Mistakes to avoid

Error rates cut, data unnamed
Fix: Competitor samples say error rates fell by a percentage with no dataset, rule count or baseline. Name the data, such as loans, call report fields or critical data elements, how many rules you ran on it, and what changed for the people who use it.
Cleanup with no root cause
Fix: Recruiters for these roles say interviews probe how you trace an issue to its source, and bank postings ask for root cause and issue management. Count the issues you resolved, say how you traced them, and how many were fixed upstream rather than patched.
Rules counted, not tied to anything
Fix: A large rule count means little unless the rules guard data that matters. Bank postings this year talk about critical data, key metrics and critical reports. Tie your rules to those, such as the elements behind a regulatory report.
Governance words, no control evidence
Fix: Data quality postings at banks ask for help with regulatory responses, self-assessments and audits. If you supplied evidence, say for which exams or audits and what the findings were. Governance framed as a result reads stronger than a list of frameworks.
Model and AI data left out
Fix: Analytics engineering surveys this year found teams putting AI into coding faster than into testing. If you check the data feeding models or AI tools, put it on the page: which models, what the gates block and how often they fired.

08Format and template

One column and one page, in the order summary, experience, education, skills, certifications. Put the data you protect and your main tools in the headline, give rule counts, issues resolved, report adjustments and audit findings as numbers, and name the critical data each rule guards. List CDMP or similar credentials with their expiry date below your experience, and keep one issue you traced end to end ready for interviews.

Shown above: The Jake's

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09Questions data quality analysts ask

What does a data quality analyst do?

O*NET has no data quality occupation, so the closest is data scientists, whose core tasks include cleaning and manipulating raw data and testing and validating models. Postings read for this page added the rest: writing data quality rules, profiling data, logging and tracing issues to their source, documenting critical data elements, and supplying evidence for audits and regulators. Banks, health agencies and data platforms all hire for it.

How much do data quality analysts make?

BLS has no separate figure, so the closest is data scientists: a median of $120,230 in May 2025, with the lowest tenth under $67,240 and the highest tenth over $199,130. Industry moves it, from a median $142,240 in publishing and broadcasting to $108,650 at insurance carriers. BLS does not separate data quality work, so compare postings for the same title in your region before you negotiate.

Which tools should a data quality analyst know?

SQL first. In O*NET's count of 2025 postings for data scientists, SQL appears in 51 percent and Python in 66, with Snowflake at 8. Data quality postings read for this page also asked for governance platforms such as Collibra, rule engines such as Informatica Data Quality, and warehouse tests in dbt or Great Expectations. Name each in the bullet where you used it.

Is the CDMP certification worth it for data quality work?

It can help, particularly at banks and other regulated firms. DAMA International's Certified Data Management Professional has levels by experience and a data quality specialty exam, and it must be renewed with professional development. CareerOneStop's 17 certifications for data scientists include none for data quality, so the CDMP is the field's own credential. List it with its level and expiry date.

How do I move from data analyst to data quality analyst?

Show the quality work you already do. Recruiters say interviews ask how you handle duplicate records, inconsistent formats and issues traced to their source, so put examples of each in your bullets: rules you wrote, duplicates you merged, and fixes you got made upstream. Add the data domain, such as loans or insurance claims, and the reports your checks protect.

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Data quality analyst resume examples & writing guide for 2026