semantic layer jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, semantic layer appears in 417 job postings indexed by Skillenai over the past 90 days — Analytics Engineer has the most postings mentioning semantic layer, with demand share up 22.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want semantic layer?
Upload your resume and Skillenai will show which roles your semantic layer experience fits, which skills you already cover, and what is missing.
Frequently asked questions about semantic layer
+Is semantic layer in demand in 2026?
Yes. semantic layer appears in 417 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Analytics Engineer accounts for the most postings mentioning semantic layer (20.1% of all postings mentioning semantic layer).
+What jobs require semantic layer?
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 semantic layer are Analytics Engineering Manager (33.3% of that role’s postings mention semantic layer), Analytics Engineering Director (22.7% of that role’s postings mention semantic layer), Analytics Engineer (10.7% of that role’s postings mention semantic layer).
+What skills are commonly paired with semantic layer?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), semantic layer most often appears alongside SQL, DBT, data modeling, Python, Snowflake.
+Where is semantic layer most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring semantic layer are New York City, London, Bengaluru, San Francisco, Berlin, according to the Skillenai jobs index.
+How can I keep up with new semantic layer content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning semantic layer alongside the jobs index. You can subscribe to a daily email digest of new semantic layer content from your Skillenai account.
+Which skills come before and after semantic layer?
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 semantic layer — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around semantic layer
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before semantic layer
Skills after semantic layer
No published outgoing skill pairs yet.
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: Spreadsheets | 1 |
| Before: ad-hoc reports | 1 |
| Before: SQL Stored Procedures | 1 |
| Before: dynamic visualizations | 1 |
| Before: database design | 1 |
| Before: dashboards | 1 |
| Before: PowerBI | 1 |
| Before: programming | 1 |
Roles most likely to require semantic layer
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Analytics Engineering Manager | 9 | 33.3% |
| Analytics Engineering Director | 5 | 22.7% |
| Analytics Engineer | 84 | 10.7% |
| Product Data Scientist | 4 | 9.5% |
| Business Intelligence Engineer | 6 | 6.4% |
| BI Engineer | 3 | 6.2% |
| Data Analytics Lead | 2 | 6.2% |
| Analytics Leader | 2 | 5.6% |
| Operations Data Analyst | 1 | 4.8% |
| Data Product Manager | 4 | 4.3% |
Roles with the most semantic layer postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Analytics Engineer | 84 | 20.1% |
| Data Engineer | 39 | 9.4% |
| Data Analyst | 30 | 7.2% |
| Data Architect | 20 | 4.8% |
| Product Manager | 20 | 4.8% |
| Software Engineer | 16 | 3.8% |
| Analytics Engineering Manager | 9 | 2.2% |
| Data Engineering Manager | 8 | 1.9% |
| Data Scientist | 8 | 1.9% |
| Business Intelligence Engineer | 6 | 1.4% |
Top companies posting jobs requiring semantic layer
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Accenture | 7 |
| Pleo | 6 |
| Discord | 5 |
| Returnly | 4 |
| Lyft | 4 |
| UpGuard | 4 |
| Port | 3 |
| ezCater | 3 |
| Capital One | 3 |
| Blend360 | 3 |
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 semantic layer
| Name | Postings | Share |
|---|---|---|
| New York City | 22 | 5.3% |
| London | 15 | 3.6% |
| Bengaluru | 11 | 2.6% |
| San Francisco | 10 | 2.4% |
| Berlin | 7 | 1.7% |
| Paris | 6 | 1.4% |
| Toronto | 6 | 1.4% |
| Boston | 5 | 1.2% |
| Hyderabad | 5 | 1.2% |
Skills commonly paired with semantic layer
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 semantic layer by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s semantic layer postings by all semantic layer 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 0.2%. 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
- d1aa992f4c43dbf0
- data_as_of
- 2026-10-29
- window_days
- 90