Apache Spark jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-05, Apache Spark appears in 2,350 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Apache Spark, with demand share down 3.3% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-05
Which roles want Apache Spark?
Upload your resume and Skillenai will show which roles your Apache Spark experience fits, which skills you already cover, and what is missing.
Frequently asked questions about Apache Spark
+Is Apache Spark in demand in 2026?
Yes. Apache Spark appears in 2,350 job postings indexed by Skillenai over the 90 days ending 2026-11-05. Data Engineer accounts for the most postings mentioning Apache Spark (33.4% of all postings mentioning Apache Spark).
+What jobs require Apache Spark?
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 Apache Spark are Big Data Engineer (30.8% of that role’s postings mention Apache Spark), Senior Data Engineer (17.9% of that role’s postings mention Apache Spark), Data Infrastructure Engineer (17.6% of that role’s postings mention Apache Spark).
+What skills are commonly paired with Apache Spark?
Across job postings indexed by Skillenai (90 days ending 2026-11-05), Apache Spark most often appears alongside Python, SQL, AWS, Databricks, CI/CD.
+Where is Apache Spark most in demand?
As of 2026-11-05, the metro areas posting the most jobs requiring Apache Spark are Bengaluru, Pune, Hyderabad, London, New York City, according to the Skillenai jobs index.
+How can I keep up with new Apache Spark content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Apache Spark alongside the jobs index. You can subscribe to a daily email digest of new Apache Spark content from your Skillenai account.
+Which skills come before and after Apache Spark?
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 Apache Spark — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Apache Spark
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Apache Spark
Skills after Apache Spark
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 | 127 |
| Before: sql | 81 |
| Before: Power BI | 65 |
| Before: Tableau | 64 |
| Before: spark | 48 |
| Before: snowflake | 43 |
| Before: PySpark | 42 |
| Before: ETL | 36 |
| After: python | 66 |
| After: snowflake | 52 |
| After: sql | 49 |
| After: PySpark | 49 |
| After: Power BI | 47 |
| After: Tableau | 38 |
| After: docker | 37 |
| After: Apache Airflow | 37 |
Roles most likely to require Apache Spark
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Big Data Engineer | 12 | 30.8% |
| Senior Data Engineer | 5 | 17.9% |
| Data Infrastructure Engineer | 6 | 17.6% |
| Cloud Data Engineer | 8 | 17.0% |
| Actuarial Analyst | 3 | 14.3% |
| Developer Relations Manager | 6 | 14.0% |
| AI/ML Architect | 3 | 13.0% |
| Data Engineering Lead | 6 | 12.8% |
| Data Engineer | 786 | 12.4% |
| Solution Engineer | 25 | 12.4% |
Roles with the most Apache Spark postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 786 | 33.4% |
| Software Engineer | 376 | 16.0% |
| Data Scientist | 132 | 5.6% |
| Solutions Architect | 117 | 5.0% |
| Forward Deployed Engineer | 68 | 2.9% |
| Data Architect | 54 | 2.3% |
| Machine Learning Engineer | 50 | 2.1% |
| AI Engineer | 48 | 2.0% |
| Data Analyst | 27 | 1.1% |
| ML Engineer | 25 | 1.1% |
Top companies posting jobs requiring Apache Spark
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Databricks | 214 |
| Mastercard | 53 |
| Cisco | 34 |
| Snowflake | 32 |
| Genpact | 25 |
| Bah | 24 |
| Experian | 24 |
| Grab | 23 |
| General Motors | 21 |
| Barclays | 19 |
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 Apache Spark
| Name | Postings | Share |
|---|---|---|
| Bengaluru | 93 | 4.0% |
| Pune | 56 | 2.4% |
| Hyderabad | 54 | 2.3% |
| London | 48 | 2.0% |
| New York City | 46 | 2.0% |
| Amsterdam | 40 | 1.7% |
| Singapore | 34 | 1.4% |
| San Francisco | 32 | 1.4% |
| Warsaw | 30 | 1.3% |
Skills commonly paired with Apache Spark
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 Apache Spark by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Apache Spark postings by all Apache Spark 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: 1.0% to 1.0%. 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
- 8941fbaa6b04f7b4
- data_as_of
- 2026-11-05
- window_days
- 90