Azure Data Factory jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-07, Azure Data Factory appears in 1,174 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Azure Data Factory, with demand share up 7.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-07
Which roles want Azure Data Factory?
Upload your resume and Skillenai will show which roles your Azure Data Factory experience fits, which skills you already cover, and what is missing.
Frequently asked questions about Azure Data Factory
+Is Azure Data Factory in demand in 2026?
Yes. Azure Data Factory appears in 1,174 job postings indexed by Skillenai over the 90 days ending 2026-11-07. Data Engineer accounts for the most postings mentioning Azure Data Factory (43.5% of all postings mentioning Azure Data Factory).
+What jobs require Azure Data Factory?
According to the Skillenai jobs index over the 90 days ending 2026-11-07, among roles with at least 20 postings, the highest shares mentioning Azure Data Factory are Azure Data Engineer (50.0% of that role’s postings mention Azure Data Factory), Power BI Developer (13.4% of that role’s postings mention Azure Data Factory), Data Engineering Lead (13.3% of that role’s postings mention Azure Data Factory).
+What skills are commonly paired with Azure Data Factory?
Across job postings indexed by Skillenai (90 days ending 2026-11-07), Azure Data Factory most often appears alongside SQL, Python, Databricks, ETL, CI/CD.
+Where is Azure Data Factory most in demand?
As of 2026-11-07, the metro areas posting the most jobs requiring Azure Data Factory are Bengaluru, Pune, Toronto, Hyderabad, London, according to the Skillenai jobs index.
+How can I keep up with new Azure Data Factory content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Azure Data Factory alongside the jobs index. You can subscribe to a daily email digest of new Azure Data Factory content from your Skillenai account.
+Which skills come before and after Azure Data Factory?
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 Azure Data Factory — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Azure Data Factory
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Azure Data Factory
Skills after Azure Data Factory
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 | 190 |
| Before: sql | 152 |
| Before: Power BI | 128 |
| Before: Tableau | 111 |
| Before: PySpark | 96 |
| Before: AWS Glue | 77 |
| Before: Kafka | 73 |
| Before: snowflake | 72 |
| After: python | 80 |
| After: sql | 73 |
| After: AWS Glue | 69 |
| After: snowflake | 66 |
| After: Power BI | 64 |
| After: Redshift | 64 |
| After: PySpark | 64 |
| After: S3 | 63 |
Roles most likely to require Azure Data Factory
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Azure Data Engineer | 16 | 50.0% |
| Power BI Developer | 13 | 13.4% |
| Data Engineering Lead | 6 | 13.3% |
| Senior Data Engineer | 3 | 11.1% |
| Cloud Data Engineer | 5 | 10.4% |
| Data Engineering Intern | 4 | 9.3% |
| AI/ML Scientist | 2 | 9.1% |
| Data Platform Architect | 3 | 8.8% |
| Data Architect | 66 | 8.6% |
| Data Engineering Director | 5 | 7.9% |
Roles with the most Azure Data Factory postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 511 | 43.5% |
| Data Architect | 66 | 5.6% |
| Software Engineer | 49 | 4.2% |
| Data Analyst | 34 | 2.9% |
| Azure Data Engineer | 16 | 1.4% |
| Data Scientist | 15 | 1.3% |
| Solution Architect | 15 | 1.3% |
| Solutions Architect | 15 | 1.3% |
| Data Engineering Manager | 13 | 1.1% |
| Power BI Developer | 13 | 1.1% |
Top companies posting jobs requiring Azure Data Factory
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| PwC | 52 |
| Accenture | 34 |
| Bosch | 25 |
| Manulife | 23 |
| Inetum | 14 |
| Mastercard | 12 |
| Tieto | 11 |
| Encora | 11 |
| Talan | 10 |
| DXC Technology | 10 |
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 Azure Data Factory
| Name | Postings | Share |
|---|---|---|
| Bengaluru | 38 | 3.2% |
| Pune | 33 | 2.8% |
| Toronto | 26 | 2.2% |
| Hyderabad | 24 | 2.0% |
| London | 23 | 2.0% |
| Cairo | 17 | 1.4% |
| Warsaw | 16 | 1.4% |
| Madrid | 12 | 1.0% |
| Bucharest | 10 | 0.9% |
Skills commonly paired with Azure Data Factory
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 2026-11-07. 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 Azure Data Factory by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Azure Data Factory postings by all Azure Data Factory 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: 0.3% to 0.4%. 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 2,471 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (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
- af73c035008c8725
- data_as_of
- 2026-11-07
- window_days
- 90