root-cause analysis jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, root-cause analysis appears in 1,181 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning root-cause analysis, with demand share up 23.7% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about root-cause analysis
+Is root-cause analysis in demand in 2026?
Yes. root-cause analysis appears in 1,181 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Software Engineer accounts for the most postings mentioning root-cause analysis (12.4% of all postings mentioning root-cause analysis).
+What jobs require root-cause analysis?
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 root-cause analysis are Vulnerability Research Engineer (15.0% of that role’s postings mention root-cause analysis), Quality Engineering Manager (12.0% of that role’s postings mention root-cause analysis), Production Engineer (11.8% of that role’s postings mention root-cause analysis).
+What skills are commonly paired with root-cause analysis?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), root-cause analysis most often appears alongside Python, SQL, CI/CD, observability, monitoring.
+Where is root-cause analysis most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring root-cause analysis are Bengaluru, San Francisco, New York City, Hyderabad, Santa Clara, according to the Skillenai jobs index.
+How can I keep up with new root-cause analysis content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning root-cause analysis alongside the jobs index. You can subscribe to a daily email digest of new root-cause analysis content from your Skillenai account.
+Which skills come before and after root-cause analysis?
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 root-cause analysis — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around root-cause analysis
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before root-cause analysis
Skills after root-cause analysis
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 | 3 |
| Before: sql | 3 |
| Before: Excel | 3 |
| Before: Azure | 2 |
| Before: marketing campaigns | 2 |
| Before: data integrity | 2 |
| Before: cohort analysis | 2 |
| Before: Google Data Studio | 1 |
| After: Unit testing | 3 |
| After: Jira | 3 |
| After: EC2 | 2 |
| After: JavaScript | 2 |
| After: a/b testing | 2 |
| After: S3 | 2 |
| After: AWS services | 1 |
| After: usability studies | 1 |
Roles most likely to require root-cause analysis
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Vulnerability Research Engineer | 3 | 15.0% |
| Quality Engineering Manager | 3 | 12.0% |
| Production Engineer | 6 | 11.8% |
| Quality Test Engineer | 3 | 10.7% |
| Systems Integration Engineer | 3 | 10.7% |
| Engineering Product Manager | 3 | 8.8% |
| Integration and Test Engineer | 2 | 8.7% |
| Founding Product Engineer | 2 | 8.3% |
| Hardware Systems Engineer | 3 | 8.1% |
| Avionics Test Engineer | 2 | 7.7% |
Roles with the most root-cause analysis postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 147 | 12.4% |
| Systems Engineer | 33 | 2.8% |
| Test Engineer | 30 | 2.5% |
| Engineering Manager | 29 | 2.5% |
| Product Manager | 23 | 1.9% |
| Program Manager | 22 | 1.9% |
| Data Analyst | 20 | 1.7% |
| Data Engineer | 17 | 1.4% |
| Business Analyst | 16 | 1.4% |
| Infrastructure Engineer | 16 | 1.4% |
Top companies posting jobs requiring root-cause analysis
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Cisco | 21 |
| Pure Storage | 16 |
| Nimble Storage | 16 |
| General Motors | 15 |
| HPE | 15 |
| JPMorgan Chase & Co. | 15 |
| Writer | 10 |
| Elsevier | 10 |
| NVIDIA | 10 |
| Globalpr | 9 |
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 root-cause analysis
| Name | Postings | Share |
|---|---|---|
| Bengaluru | 36 | 3.0% |
| San Francisco | 32 | 2.7% |
| New York City | 27 | 2.3% |
| Hyderabad | 24 | 2.0% |
| Santa Clara | 19 | 1.6% |
| London | 16 | 1.4% |
| Pune | 15 | 1.3% |
| Singapore | 15 | 1.3% |
| Austin | 14 | 1.2% |
Skills commonly paired with root-cause analysis
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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 root-cause analysis by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s root-cause analysis postings by all root-cause analysis 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 0.5%. 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
- 4697b9f913216904
- 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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