Databricks Workflows jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-30, Databricks Workflows appears in 201 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Databricks Workflows, with demand share up 19.6% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-30
Which roles want Databricks Workflows?
Upload your resume and Skillenai will show which roles your Databricks Workflows experience fits, which skills you already cover, and what is missing.
Frequently asked questions about Databricks Workflows
+Is Databricks Workflows in demand in 2026?
Yes. Databricks Workflows appears in 201 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Data Engineer accounts for the most postings mentioning Databricks Workflows (54.2% of all postings mentioning Databricks Workflows).
+What jobs require Databricks Workflows?
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 Databricks Workflows are DataOps Engineer (10.0% of that role’s postings mention Databricks Workflows), ML Ops Engineer (5.6% of that role’s postings mention Databricks Workflows), Finance Manager (4.8% of that role’s postings mention Databricks Workflows).
+What skills are commonly paired with Databricks Workflows?
Across job postings indexed by Skillenai (90 days ending 2026-10-30), Databricks Workflows most often appears alongside Delta Lake, Databricks, SQL, Python, PySpark.
+Where is Databricks Workflows most in demand?
As of 2026-10-30, the metro areas posting the most jobs requiring Databricks Workflows are Pune, Bengaluru, McLean, Seattle, Toronto, according to the Skillenai jobs index.
+How can I keep up with new Databricks Workflows content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Databricks Workflows alongside the jobs index. You can subscribe to a daily email digest of new Databricks Workflows content from your Skillenai account.
+Which skills come before and after Databricks Workflows?
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 Databricks Workflows — last 90 days
Career paths around Databricks Workflows
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Databricks Workflows
Skills after Databricks Workflows
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: PySpark | 6 |
| Before: python | 5 |
| Before: sql | 5 |
| Before: spark | 4 |
| Before: snowflake | 4 |
| Before: ETL | 3 |
| Before: Sqoop | 3 |
| Before: S3 | 3 |
| After: PySpark | 2 |
| After: Azure Databricks | 2 |
| After: Informatica Cloud | 2 |
| After: python | 2 |
| After: Azure Stream Analytics | 2 |
| After: Azure SQL Database | 2 |
| After: SSIS | 1 |
| After: Grafana | 1 |
Roles most likely to require Databricks Workflows
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| DataOps Engineer | 2 | 10.0% |
| ML Ops Engineer | 2 | 5.6% |
| Finance Manager | 1 | 4.8% |
| Technical Manager | 1 | 4.8% |
| AI Infrastructure Architect | 1 | 3.7% |
| Senior Data Scientist | 1 | 3.7% |
| Senior Data Engineer | 1 | 3.4% |
| Data Platform Architect | 1 | 3.2% |
| Azure Data Engineer | 1 | 2.7% |
| Data Engineer | 109 | 1.7% |
Roles with the most Databricks Workflows postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 109 | 54.2% |
| Data Architect | 11 | 5.5% |
| ML Engineer | 4 | 2.0% |
| Platform Engineer | 4 | 2.0% |
| Analytics Engineer | 3 | 1.5% |
| Data Engineering Architect | 3 | 1.5% |
| Data Engineering Manager | 3 | 1.5% |
| Data Scientist | 3 | 1.5% |
| Databricks Data Engineer | 3 | 1.5% |
| Software Engineer | 3 | 1.5% |
Top companies posting jobs requiring Databricks Workflows
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| The Nielsen Company | 6 |
| Bosch | 5 |
| Lumenalta | 5 |
| Ultra Tendency | 4 |
| Steampunk | 4 |
| Protective | 4 |
| Inetum | 4 |
| Life360 | 4 |
| Grammarly | 3 |
| Codvo-team | 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 Databricks Workflows
| Name | Postings | Share |
|---|---|---|
| Pune | 10 | 5.0% |
| Bengaluru | 8 | 4.0% |
| McLean | 4 | 2.0% |
| Seattle | 4 | 2.0% |
| Toronto | 4 | 2.0% |
| Birmingham | 3 | 1.5% |
| Chennai | 3 | 1.5% |
| Coimbatore | 3 | 1.5% |
| Hyderabad | 3 | 1.5% |
Skills commonly paired with Databricks Workflows
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 Databricks Workflows by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Databricks Workflows postings by all Databricks Workflows 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
- e46205209aba87dd
- data_as_of
- 2026-10-30
- window_days
- 90