graph databases jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-30, graph databases appears in 419 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning graph databases, with demand share up 10.5% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-30
Which roles want graph databases?
Upload your resume and Skillenai will show which roles your graph databases experience fits, which skills you already cover, and what is missing.
Frequently asked questions about graph databases
+Is graph databases in demand in 2026?
Yes. graph databases appears in 419 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Software Engineer accounts for the most postings mentioning graph databases (25.8% of all postings mentioning graph databases).
+What jobs require graph databases?
According to the Skillenai jobs index over the 90 days ending 2026-10-30, among roles with at least 20 postings, the highest shares mentioning graph databases are Consulting Engineer (7.4% of that role’s postings mention graph databases), Data Modeler (6.7% of that role’s postings mention graph databases), AI Application Engineer (6.2% of that role’s postings mention graph databases).
+What skills are commonly paired with graph databases?
Across job postings indexed by Skillenai (90 days ending 2026-10-30), graph databases most often appears alongside Python, AWS, SQL, CI/CD, Kubernetes.
+Where is graph databases most in demand?
As of 2026-10-30, the metro areas posting the most jobs requiring graph databases are New York City, Hyderabad, San Francisco, London, Bengaluru, according to the Skillenai jobs index.
+How can I keep up with new graph databases content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning graph databases alongside the jobs index. You can subscribe to a daily email digest of new graph databases content from your Skillenai account.
+Which skills come before and after graph databases?
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 graph databases — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around graph databases
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before graph databases
Skills after graph databases
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 | 2 |
| Before: spark | 1 |
| Before: Open AI | 1 |
| Before: performance monitoring | 1 |
| Before: Selenium with Python | 1 |
| Before: cosine similarity | 1 |
| Before: Pickle | 1 |
| After: python | 3 |
| After: spark | 1 |
| After: Open Search | 1 |
| After: ETL | 1 |
| After: Grafana | 1 |
| After: supervised learning | 1 |
| After: CloudFormation | 1 |
| After: SOLID principles | 1 |
Roles most likely to require graph databases
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Consulting Engineer | 4 | 7.4% |
| Data Modeler | 2 | 6.7% |
| AI Application Engineer | 2 | 6.2% |
| Distributed Systems Engineer | 4 | 5.6% |
| ML Researcher | 1 | 5.0% |
| People Analytics Analyst | 1 | 5.0% |
| AI/ML Architect | 1 | 4.5% |
| Quantitative Finance Analyst | 1 | 4.2% |
| Agentic AI Engineer | 2 | 3.6% |
| Principal Software Engineer | 5 | 3.5% |
Roles with the most graph databases postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 108 | 25.8% |
| Data Engineer | 33 | 7.9% |
| Data Scientist | 20 | 4.8% |
| Backend Engineer | 15 | 3.6% |
| AI Engineer | 14 | 3.3% |
| Data Architect | 13 | 3.1% |
| Analytics Engineer | 12 | 2.9% |
| ML Engineer | 11 | 2.6% |
| Engineering Manager | 9 | 2.1% |
| Machine Learning Engineer | 9 | 2.1% |
Top companies posting jobs requiring graph databases
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| CLERA | 17 |
| CrowdStrike | 14 |
| Neo4j | 14 |
| Roche | 9 |
| Cisco | 9 |
| Horizon3.ai | 8 |
| Experian | 8 |
| ServiceNow | 7 |
| Accenture | 7 |
| Adyen | 6 |
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 graph databases
| Name | Postings | Share |
|---|---|---|
| New York City | 18 | 4.3% |
| Hyderabad | 11 | 2.6% |
| San Francisco | 11 | 2.6% |
| London | 10 | 2.4% |
| Bengaluru | 9 | 2.1% |
| Amsterdam | 8 | 1.9% |
| San Jose | 8 | 1.9% |
| San Mateo | 7 | 1.7% |
| Budapest | 6 | 1.4% |
Skills commonly paired with graph databases
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 2026-10-30. 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 graph databases by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s graph databases postings by all graph databases 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: 0.2% 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
- 8265373252587b2f
- data_as_of
- 2026-10-30
- window_days
- 90