Data transformation jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, Data transformation appears in 2,263 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Data transformation, with demand share up 3.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about Data transformation
+Is Data transformation in demand in 2026?
Yes. Data transformation appears in 2,263 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Engineer accounts for the most postings mentioning Data transformation (20.0% of all postings mentioning Data transformation).
+What jobs require Data transformation?
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 transformation are Generative AI Specialist (46.6% of that role’s postings mention Data transformation), Agent Architect (31.4% of that role’s postings mention Data transformation), Finance Data Analyst (22.2% of that role’s postings mention Data transformation).
+What skills are commonly paired with Data transformation?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), Data transformation most often appears alongside SQL, Python, data modeling, data pipelines, ETL.
+Where is Data transformation most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring Data transformation are New York City, London, Toronto, Bengaluru, San Francisco, according to the Skillenai jobs index.
+How can I keep up with new Data transformation content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Data transformation alongside the jobs index. You can subscribe to a daily email digest of new Data transformation content from your Skillenai account.
+Which skills come before and after Data transformation?
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 transformation — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Data transformation
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Data transformation
Skills after Data transformation
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 | 36 |
| Before: sql | 35 |
| Before: Tableau | 20 |
| Before: Excel | 16 |
| Before: Power BI | 14 |
| Before: data analysis | 12 |
| Before: snowflake | 11 |
| Before: ETL | 10 |
| After: python | 18 |
| After: Power BI | 16 |
| After: sql | 15 |
| After: Tableau | 14 |
| After: stored procedures | 13 |
| After: snowflake | 12 |
| After: ETL | 12 |
| After: PySpark | 10 |
Roles most likely to require Data transformation
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Generative AI Specialist | 27 | 46.6% |
| Agent Architect | 11 | 31.4% |
| Finance Data Analyst | 6 | 22.2% |
| Data Engineering Lead | 14 | 19.4% |
| Integration Developer | 4 | 19.0% |
| IT Systems Administrator | 5 | 18.5% |
| People Analytics Analyst | 4 | 14.8% |
| Actuarial Analyst | 3 | 14.3% |
| Data & Analytics Engineer | 3 | 14.3% |
| Lead Data Engineer | 10 | 13.3% |
Roles with the most Data transformation postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 452 | 20.0% |
| Data Analyst | 211 | 9.3% |
| Data Scientist | 133 | 5.9% |
| Software Engineer | 120 | 5.3% |
| Analytics Engineer | 85 | 3.8% |
| Data Architect | 50 | 2.2% |
| Business Analyst | 42 | 1.9% |
| Product Manager | 41 | 1.8% |
| Generative AI Specialist | 27 | 1.2% |
| Business Intelligence Analyst | 24 | 1.1% |
Top companies posting jobs requiring Data transformation
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Barclays | 34 |
| Innodata | 32 |
| PwC | 31 |
| Capco | 23 |
| WPP | 22 |
| Ebury | 16 |
| General Dynamics Information Technology | 16 |
| Blend360 | 13 |
| Nigel Frank | 13 |
| Parloa | 13 |
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 transformation
| Name | Postings | Share |
|---|---|---|
| New York City | 75 | 3.3% |
| London | 58 | 2.6% |
| Toronto | 44 | 1.9% |
| Bengaluru | 43 | 1.9% |
| San Francisco | 36 | 1.6% |
| Chicago | 29 | 1.3% |
| Pune | 27 | 1.2% |
| Boston | 26 | 1.1% |
| Hyderabad | 23 | 1.0% |
Skills commonly paired with Data transformation
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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 transformation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Data transformation postings by all Data transformation 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: 0.9% to 0.9%. 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 2,596 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
- cde13a3121e96a79
- 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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