data manipulation jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, data manipulation appears in 481 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning data manipulation, with demand share up 0.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about data manipulation
+Is data manipulation in demand in 2026?
Yes. data manipulation appears in 481 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Scientist accounts for the most postings mentioning data manipulation (16.0% of all postings mentioning data manipulation).
+What jobs require data manipulation?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning data manipulation are Actuarial Analyst (19.0% of that role’s postings mention data manipulation), Implementation Engineer (16.3% of that role’s postings mention data manipulation), Risk Analyst (12.2% of that role’s postings mention data manipulation).
+What skills are commonly paired with data manipulation?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), data manipulation most often appears alongside Python, SQL, data visualization, R, Data analysis.
+Where is data manipulation most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring data manipulation are Bengaluru, New York City, Toronto, Arlington, San Francisco, according to the Skillenai jobs index.
+How can I keep up with new data manipulation content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning data manipulation alongside the jobs index. You can subscribe to a daily email digest of new data manipulation content from your Skillenai account.
+Which skills come before and after data manipulation?
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 manipulation — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around data manipulation
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before data manipulation
Skills after data manipulation
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: python | 11 |
| Before: sql | 9 |
| Before: Excel | 6 |
| Before: Power BI | 4 |
| Before: Tableau | 4 |
| Before: PySpark | 3 |
| Before: SQL Server | 3 |
| Before: spark | 2 |
| After: data pipelines | 6 |
| After: Power BI | 6 |
| After: Tableau | 5 |
| After: Excel | 5 |
| After: python | 4 |
| After: dashboards | 4 |
| After: ETL | 4 |
| After: reporting | 3 |
Roles most likely to require data manipulation
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Actuarial Analyst | 4 | 19.0% |
| Implementation Engineer | 8 | 16.3% |
| Risk Analyst | 5 | 12.2% |
| Decision Scientist | 3 | 7.0% |
| Analytics Developer | 1 | 4.8% |
| Marketing Data Scientist | 1 | 4.8% |
| Quantitative Risk Analyst | 1 | 4.5% |
| Analytics Analyst | 2 | 4.0% |
| Data Security Engineer | 1 | 3.8% |
| Analytics Intern | 1 | 3.6% |
Roles with the most data manipulation postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 77 | 16.0% |
| Data Analyst | 65 | 13.5% |
| Business Analyst | 23 | 4.8% |
| Software Engineer | 21 | 4.4% |
| Product Manager | 17 | 3.5% |
| Data Engineer | 9 | 1.9% |
| Implementation Engineer | 8 | 1.7% |
| Analyst | 5 | 1.0% |
| Business Intelligence Analyst | 5 | 1.0% |
| Research Scientist | 5 | 1.0% |
Top companies posting jobs requiring data manipulation
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Elsevier | 16 |
| ezCater | 11 |
| WPP | 8 |
| Clarity Innovations | 8 |
| Air | 8 |
| PointClickCare | 7 |
| 5 | |
| City of New York | 5 |
| Rocket Lab | 5 |
| Globalpr | 4 |
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 manipulation
| Name | Postings | Share |
|---|---|---|
| Bengaluru | 18 | 3.7% |
| New York City | 18 | 3.7% |
| Toronto | 16 | 3.3% |
| Arlington | 12 | 2.5% |
| San Francisco | 11 | 2.3% |
| London | 9 | 1.9% |
| Boston | 8 | 1.7% |
| Long Beach | 8 | 1.7% |
| Chicago | 6 | 1.2% |
Skills commonly paired with data manipulation
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. 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 manipulation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s data manipulation postings by all data manipulation 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: 0.2% to 0.2%. 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 2,595 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (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
- 48555f673d9c80d9
- data_as_of
- 2026-09-30
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
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