PySpark jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, PySpark appears in 2,319 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning PySpark, with demand share up 6.6% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
Which roles want PySpark?
Upload your resume and Skillenai will show which roles your PySpark experience fits, which skills you already cover, and what is missing.
Prepare to discuss PySpark in your interview
We’re building mock interviews informed by job postings and career profiles, to help you explain how you’ve used PySpark.
Frequently asked questions about PySpark
+Is PySpark in demand in 2026?
Yes. PySpark appears in 2,319 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Engineer accounts for the most postings mentioning PySpark (38.1% of all postings mentioning PySpark).
+What jobs require PySpark?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning PySpark are Senior Data Engineer (36.1% of that role’s postings mention PySpark), Azure Data Engineer (35.1% of that role’s postings mention PySpark), Data Platform Architect (21.6% of that role’s postings mention PySpark).
+What skills are commonly paired with PySpark?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), PySpark most often appears alongside Python, SQL, Databricks, ETL, AWS.
+Where is PySpark most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring PySpark are Bengaluru, Pune, Hyderabad, Toronto, New York City, according to the Skillenai jobs index.
+How can I keep up with new PySpark content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning PySpark alongside the jobs index. You can subscribe to a daily email digest of new PySpark content from your Skillenai account.
+Which skills come before and after PySpark?
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 PySpark — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around PySpark
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before PySpark
Skills after PySpark
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 | 237 |
| Before: sql | 146 |
| Before: Tableau | 119 |
| Before: Power BI | 108 |
| Before: spark | 95 |
| Before: snowflake | 92 |
| Before: Kafka | 77 |
| Before: Hive | 62 |
| After: snowflake | 116 |
| After: python | 116 |
| After: Power BI | 90 |
| After: databricks | 89 |
| After: Azure Data Factory | 89 |
| After: AWS Glue | 81 |
| After: spark | 81 |
| After: airflow | 78 |
Roles most likely to require PySpark
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Senior Data Engineer | 13 | 36.1% |
| Azure Data Engineer | 13 | 35.1% |
| Data Platform Architect | 8 | 21.6% |
| Machine Learning Platform Engineer | 5 | 20.8% |
| Data Engineering Intern | 6 | 16.2% |
| Cloud Data Engineer | 9 | 16.1% |
| Senior Data Scientist | 4 | 14.8% |
| Data Science Analyst | 5 | 14.7% |
| Data Science Engineer | 6 | 14.3% |
| Data Engineer | 884 | 14.2% |
Roles with the most PySpark postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 884 | 38.1% |
| Data Scientist | 247 | 10.7% |
| Software Engineer | 150 | 6.5% |
| Data Analyst | 89 | 3.8% |
| Machine Learning Engineer | 53 | 2.3% |
| Data Architect | 33 | 1.4% |
| Solutions Architect | 26 | 1.1% |
| Data Engineering Manager | 25 | 1.1% |
| ML Engineer | 25 | 1.1% |
| Analytics Engineer | 14 | 0.6% |
Top companies posting jobs requiring PySpark
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Barclays | 60 |
| Mastercard | 45 |
| Accenture | 36 |
| Adyen | 29 |
| Databricks | 29 |
| Capco | 29 |
| Devoteam | 29 |
| Stripe | 27 |
| Bosch | 27 |
| Sopra Steria | 24 |
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 PySpark
| Name | Postings | Share |
|---|---|---|
| Bengaluru | 81 | 3.5% |
| Pune | 60 | 2.6% |
| Hyderabad | 52 | 2.2% |
| Toronto | 44 | 1.9% |
| New York City | 41 | 1.8% |
| London | 39 | 1.7% |
| Amsterdam | 28 | 1.2% |
| Dublin | 28 | 1.2% |
| Chennai | 23 | 1.0% |
Skills commonly paired with PySpark
Get a daily email digest of new PySpark content
Skillenai indexes news articles, blog posts, and research papers that mention PySpark. Click below and we'll open a pre-filled daily digest — change the cadence to hourly or weekly if you prefer, then save. Free account required (~30 seconds).
Explore related pages
How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. 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 PySpark by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s PySpark postings by all PySpark 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: 0.9% 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 2,596 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (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
- 8c0acfcdab919eb7
- data_as_of
- 2026-09-30
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
Skillenai for recruiters →