feature engineering jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-05, feature engineering appears in 1,583 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning feature engineering, with demand share up 2.9% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-05
Which roles want feature engineering?
Upload your resume and Skillenai will show which roles your feature engineering experience fits, which skills you already cover, and what is missing.
Frequently asked questions about feature engineering
+Is feature engineering in demand in 2026?
Yes. feature engineering appears in 1,583 job postings indexed by Skillenai over the 90 days ending 2026-11-05. Data Scientist accounts for the most postings mentioning feature engineering (30.8% of all postings mentioning feature engineering).
+What jobs require feature engineering?
According to the Skillenai jobs index over the 90 days ending 2026-11-05, among roles with at least 20 postings, the highest shares mentioning feature engineering are Machine Learning Scientist (14.8% of that role’s postings mention feature engineering), Senior Data Scientist (14.8% of that role’s postings mention feature engineering), Machine Learning Manager (12.5% of that role’s postings mention feature engineering).
+What skills are commonly paired with feature engineering?
Across job postings indexed by Skillenai (90 days ending 2026-11-05), feature engineering most often appears alongside Python, machine learning, SQL, PyTorch, TensorFlow.
+Where is feature engineering most in demand?
As of 2026-11-05, the metro areas posting the most jobs requiring feature engineering are London, New York City, Bengaluru, San Francisco, Singapore, according to the Skillenai jobs index.
+How can I keep up with new feature engineering content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning feature engineering alongside the jobs index. You can subscribe to a daily email digest of new feature engineering content from your Skillenai account.
+Which skills come before and after feature engineering?
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 feature engineering — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around feature engineering
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before feature engineering
Skills after feature engineering
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 | 71 |
| Before: sql | 40 |
| Before: Tableau | 36 |
| Before: Power BI | 20 |
| Before: scikit-learn | 17 |
| Before: pandas | 17 |
| Before: AWS | 12 |
| Before: xgboost | 12 |
| After: python | 30 |
| After: Tableau | 23 |
| After: sql | 23 |
| After: Power BI | 22 |
| After: langchain | 14 |
| After: tensorflow | 14 |
| After: pytorch | 13 |
| After: docker | 13 |
Roles most likely to require feature engineering
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Machine Learning Scientist | 19 | 14.8% |
| Senior Data Scientist | 4 | 14.8% |
| Machine Learning Manager | 3 | 12.5% |
| Lead Data Scientist | 10 | 11.8% |
| Machine Learning Engineer | 242 | 10.5% |
| AI / ML Engineer | 7 | 10.3% |
| Data Analytics Consultant | 3 | 10.0% |
| Data Scientist | 488 | 9.2% |
| Risk Analyst | 7 | 9.0% |
| AI/ML Architect | 2 | 8.7% |
Roles with the most feature engineering postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 488 | 30.8% |
| Machine Learning Engineer | 242 | 15.3% |
| ML Engineer | 98 | 6.2% |
| Software Engineer | 81 | 5.1% |
| AI Engineer | 71 | 4.5% |
| Data Engineer | 53 | 3.3% |
| AI/ML Engineer | 24 | 1.5% |
| Machine Learning Scientist | 19 | 1.2% |
| Applied Scientist | 17 | 1.1% |
| Data Analyst | 15 | 0.9% |
Top companies posting jobs requiring feature engineering
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Wise | 47 |
| Mastercard | 40 |
| Bosch | 28 |
| Expedia Group | 25 |
| Airbnb | 19 |
| Elsevier | 16 |
| CLERA | 15 |
| Amgen | 15 |
| Adobe | 14 |
| Experian | 14 |
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 feature engineering
| Name | Postings | Share |
|---|---|---|
| London | 53 | 3.3% |
| New York City | 53 | 3.3% |
| Bengaluru | 51 | 3.2% |
| San Francisco | 49 | 3.1% |
| Singapore | 34 | 2.1% |
| Hyderabad | 32 | 2.0% |
| Pune | 22 | 1.4% |
| Toronto | 21 | 1.3% |
| Seattle | 20 | 1.3% |
Skills commonly paired with feature engineering
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 2026-11-05. 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 feature engineering by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s feature engineering postings by all feature engineering 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: 0.6% to 0.7%. 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 2,472 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
- e94571021061c2b0
- data_as_of
- 2026-11-05
- window_days
- 90