data observability jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-30, data observability appears in 272 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning data observability, with demand share up 4.3% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-30
Which roles want data observability?
Upload your resume and Skillenai will show which roles your data observability experience fits, which skills you already cover, and what is missing.
Frequently asked questions about data observability
+Is data observability in demand in 2026?
Yes. data observability appears in 272 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Data Engineer accounts for the most postings mentioning data observability (33.5% of all postings mentioning data observability).
+What jobs require data observability?
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 data observability are Data Solutions Architect (9.1% of that role’s postings mention data observability), Data Governance Manager (7.4% of that role’s postings mention data observability), Data Engineering Lead (5.8% of that role’s postings mention data observability).
+What skills are commonly paired with data observability?
Across job postings indexed by Skillenai (90 days ending 2026-10-30), data observability most often appears alongside SQL, Python, data governance, DBT, data quality.
+Where is data observability most in demand?
As of 2026-10-30, the metro areas posting the most jobs requiring data observability are London, New York City, San Francisco, Toronto, São Paulo, according to the Skillenai jobs index.
+How can I keep up with new data observability content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning data observability alongside the jobs index. You can subscribe to a daily email digest of new data observability content from your Skillenai account.
+Which skills come before and after data observability?
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 data observability — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around data observability
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before data observability
Skills after data observability
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: ETL pipelines | 1 |
| Before: report templates | 1 |
| Before: Data ingestion | 1 |
| Before: ad APIs | 1 |
| Before: interactive dashboards | 1 |
| Before: data integration | 1 |
| Before: data warehouse | 1 |
| Before: Big Data technologies | 1 |
Roles most likely to require data observability
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Data Solutions Architect | 2 | 9.1% |
| Data Governance Manager | 2 | 7.4% |
| Data Engineering Lead | 3 | 5.8% |
| Business Intelligence Engineer | 5 | 5.4% |
| DataOps Engineer | 1 | 5.0% |
| Data Engineering Director | 3 | 4.5% |
| Analytics Engineering Director | 1 | 4.5% |
| Data Product Manager | 4 | 4.3% |
| Data Engineering Manager | 10 | 4.2% |
| Analytics Engineering Manager | 1 | 3.7% |
Roles with the most data observability postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 91 | 33.5% |
| Analytics Engineer | 26 | 9.6% |
| Data Architect | 15 | 5.5% |
| Data Engineering Manager | 10 | 3.7% |
| Product Manager | 8 | 2.9% |
| Data Platform Engineer | 7 | 2.6% |
| Engineering Manager | 6 | 2.2% |
| Solutions Architect | 6 | 2.2% |
| Business Intelligence Engineer | 5 | 1.8% |
| Software Engineer | 5 | 1.8% |
Top companies posting jobs requiring data observability
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Twilio | 9 |
| Databricks | 5 |
| Capital One | 5 |
| ABOUT YOU SE & Co. KG | 5 |
| Mastercard | 4 |
| Dropbox | 4 |
| Roche | 4 |
| Cisco | 4 |
| PA Consulting | 3 |
| ezCater | 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 data observability
| Name | Postings | Share |
|---|---|---|
| London | 13 | 4.8% |
| New York City | 11 | 4.0% |
| San Francisco | 10 | 3.7% |
| Toronto | 7 | 2.6% |
| São Paulo | 5 | 1.8% |
| Hamburg | 4 | 1.5% |
| Pune | 4 | 1.5% |
| Bengaluru | 3 | 1.1% |
| Hyderabad | 3 | 1.1% |
Skills commonly paired with data observability
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 data observability by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s data observability postings by all data observability 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
- e307a82bd85568f7
- data_as_of
- 2026-10-30
- window_days
- 90