unstructured data jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, unstructured data appears in 148 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning unstructured data, with demand share down 16.4% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want unstructured data?
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Frequently asked questions about unstructured data
+Is unstructured data in demand in 2026?
Yes. unstructured data appears in 148 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Data Scientist accounts for the most postings mentioning unstructured data (14.2% of all postings mentioning unstructured data).
+What jobs require unstructured data?
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 unstructured data are Data Scientist Intern (12.6% of that role’s postings mention unstructured data), Business Intelligence Lead (4.2% of that role’s postings mention unstructured data), Quantitative Risk Analyst (4.2% of that role’s postings mention unstructured data).
+What skills are commonly paired with unstructured data?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), unstructured data most often appears alongside structured data, Python, data pipelines, machine learning, data visualization.
+Where is unstructured data most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring unstructured data are McLean, New York City, Bengaluru, Atlanta, Austin, according to the Skillenai jobs index.
+How can I keep up with new unstructured data content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning unstructured data alongside the jobs index. You can subscribe to a daily email digest of new unstructured data content from your Skillenai account.
+Which skills come before and after unstructured data?
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 unstructured data — last 90 days
Career paths around unstructured data
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before unstructured data
Skills after unstructured data
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: ETL | 2 |
| Before: python | 2 |
| Before: sql | 2 |
| Before: SAP | 2 |
| Before: Excel | 2 |
| Before: R | 2 |
| Before: Correlation | 1 |
| Before: VBA | 1 |
| After: OCR | 2 |
| After: NLP | 2 |
| After: sql | 2 |
| After: Slack | 1 |
| After: data profiling | 1 |
| After: data extraction and loading | 1 |
| After: predictive analytics | 1 |
| After: CONUS | 1 |
Roles most likely to require unstructured data
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Data Scientist Intern | 11 | 12.6% |
| Business Intelligence Lead | 1 | 4.2% |
| Quantitative Risk Analyst | 1 | 4.2% |
| Data Security Engineer | 1 | 3.6% |
| Quantitative Strategist | 1 | 3.3% |
| AI Analyst | 1 | 3.1% |
| Data Engineering Director | 2 | 2.9% |
| Forward Deployed Software Engineer | 1 | 2.0% |
| Data Science Intern | 1 | 1.9% |
| Data Science Director | 1 | 1.6% |
Roles with the most unstructured data postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 21 | 14.2% |
| Data Scientist Intern | 11 | 7.4% |
| AI Engineer | 10 | 6.8% |
| Data Engineer | 10 | 6.8% |
| Machine Learning Engineer | 7 | 4.7% |
| Product Manager | 5 | 3.4% |
| Data Analyst | 4 | 2.7% |
| Forward Deployed Engineer | 4 | 2.7% |
| AI/LLM Engineer | 3 | 2.0% |
| Backend Engineer | 3 | 2.0% |
Top companies posting jobs requiring unstructured data
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Bah | 15 |
| CLERA | 4 |
| Parsons Corporation | 3 |
| Fifth Third Bank | 3 |
| Elastic | 3 |
| Kalepa | 3 |
| 2 | |
| IQ-EQ | 2 |
| Intelligence Consulting Enterprise Solutions, Inc. (ICES) | 2 |
| Humana | 2 |
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 unstructured data
| Name | Postings | Share |
|---|---|---|
| McLean | 6 | 4.1% |
| New York City | 6 | 4.1% |
| Bengaluru | 4 | 2.7% |
| Atlanta | 3 | 2.0% |
| Austin | 3 | 2.0% |
| Barcelona | 3 | 2.0% |
| Cambridge | 3 | 2.0% |
| Cincinnati | 3 | 2.0% |
| Dublin | 3 | 2.0% |
Skills commonly paired with unstructured data
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 unstructured data by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s unstructured data postings by all unstructured data 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: 0.1% to 0.1%. 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
- 46c2d10c35577d2f
- data_as_of
- 2026-10-29
- window_days
- 90