data cleaning jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, data cleaning appears in 895 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning data cleaning, with demand share down 7.5% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want data cleaning?
Upload your resume and Skillenai will show which roles your data cleaning experience fits, which skills you already cover, and what is missing.
Frequently asked questions about data cleaning
+Is data cleaning in demand in 2026?
Yes. data cleaning appears in 895 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Data Scientist accounts for the most postings mentioning data cleaning (23.1% of all postings mentioning data cleaning).
+What jobs require data cleaning?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning data cleaning are Data Scientist Intern (23.0% of that role’s postings mention data cleaning), Data Analytics Intern (17.3% of that role’s postings mention data cleaning), People Analytics Analyst (15.0% of that role’s postings mention data cleaning).
+What skills are commonly paired with data cleaning?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), data cleaning most often appears alongside Python, SQL, data visualization, R, machine learning.
+Where is data cleaning most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring data cleaning are New York City, Toronto, Boston, San Francisco, Singapore, according to the Skillenai jobs index.
+How can I keep up with new data cleaning content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning data cleaning alongside the jobs index. You can subscribe to a daily email digest of new data cleaning content from your Skillenai account.
+Which skills come before and after data cleaning?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring data cleaning — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around data cleaning
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before data cleaning
Skills after data cleaning
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: sql | 43 |
| Before: python | 40 |
| Before: Excel | 32 |
| Before: Tableau | 26 |
| Before: Power BI | 21 |
| Before: data analysis | 17 |
| Before: Data Visualization | 10 |
| Before: dashboards | 10 |
| After: python | 44 |
| After: sql | 40 |
| After: Tableau | 31 |
| After: Power BI | 26 |
| After: dashboards | 18 |
| After: ETL | 18 |
| After: machine learning | 16 |
| After: Excel | 15 |
Roles most likely to require data cleaning
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Data Scientist Intern | 20 | 23.0% |
| Data Analytics Intern | 9 | 17.3% |
| People Analytics Analyst | 3 | 15.0% |
| Clinical Data Scientist | 3 | 13.0% |
| Data Analyst Intern | 7 | 11.9% |
| Data Science Intern | 6 | 11.1% |
| Data Analytics Consultant | 3 | 9.1% |
| Clinical Data Analyst | 2 | 8.7% |
| Analytics Analyst | 3 | 8.3% |
| Business Intelligence Intern | 2 | 8.0% |
Roles with the most data cleaning postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 207 | 23.1% |
| Data Analyst | 168 | 18.8% |
| Data Engineer | 37 | 4.1% |
| Data Scientist Intern | 20 | 2.2% |
| AI Engineer | 15 | 1.7% |
| Business Analyst | 14 | 1.6% |
| Software Engineer | 12 | 1.3% |
| Machine Learning Engineer | 11 | 1.2% |
| Business Intelligence Analyst | 9 | 1.0% |
| Data Analytics Intern | 9 | 1.0% |
Top companies posting jobs requiring data cleaning
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Bah | 65 |
| Barclays | 32 |
| Shift Technology | 10 |
| NielsenIQ | 9 |
| Guidehouse | 9 |
| WPP | 8 |
| General Dynamics Information Technology | 8 |
| City of New York | 7 |
| Western Digital | 7 |
| Bosch | 7 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for data cleaning
| Name | Postings | Share |
|---|---|---|
| New York City | 22 | 2.5% |
| Toronto | 20 | 2.2% |
| Boston | 18 | 2.0% |
| San Francisco | 17 | 1.9% |
| Singapore | 17 | 1.9% |
| Washington | 15 | 1.7% |
| Arlington | 14 | 1.6% |
| Gurugram | 14 | 1.6% |
| Paris | 13 | 1.5% |
Skills commonly paired with data cleaning
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-29. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning data cleaning by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s data cleaning postings by all data cleaning postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.4% to 0.3%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 5a1531fc75fb85b2
- data_as_of
- 2026-10-29
- window_days
- 90