distributed data processing jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-30, distributed data processing appears in 275 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning distributed data processing, with demand share down 7.5% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-30
Which roles want distributed data processing?
Upload your resume and Skillenai will show which roles your distributed data processing experience fits, which skills you already cover, and what is missing.
Frequently asked questions about distributed data processing
+Is distributed data processing in demand in 2026?
Yes. distributed data processing appears in 275 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Data Engineer accounts for the most postings mentioning distributed data processing (40.4% of all postings mentioning distributed data processing).
+What jobs require distributed data processing?
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 distributed data processing are AI/ML Scientist (13.6% of that role’s postings mention distributed data processing), Machine Learning Research Engineer (10.0% of that role’s postings mention distributed data processing), Senior Data Engineer (6.9% of that role’s postings mention distributed data processing).
+What skills are commonly paired with distributed data processing?
Across job postings indexed by Skillenai (90 days ending 2026-10-30), distributed data processing most often appears alongside Python, SQL, AWS, ETL, Databricks.
+Where is distributed data processing most in demand?
As of 2026-10-30, the metro areas posting the most jobs requiring distributed data processing are London, New York City, San Francisco, Hyderabad, Bengaluru, according to the Skillenai jobs index.
+How can I keep up with new distributed data processing content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning distributed data processing alongside the jobs index. You can subscribe to a daily email digest of new distributed data processing content from your Skillenai account.
+Which skills come before and after distributed data processing?
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 distributed data processing — last 90 days
Career paths around distributed data processing
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before distributed data processing
Skills after distributed data processing
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: MYSQL | 1 |
| Before: Java 8 | 1 |
| Before: Highcharts | 1 |
| Before: Multithreading | 1 |
| Before: Apache Solr | 1 |
| Before: Bootstrap | 1 |
| Before: Apache Tika | 1 |
| Before: Hamming code | 1 |
| After: machine learning algorithms | 1 |
| After: reliability | 1 |
| After: Agile processes | 1 |
| After: threat detection capabilities | 1 |
| After: cybersecurity solutions | 1 |
| After: cloud based systems | 1 |
| After: Azure Active Directory | 1 |
| After: Azure Data Lake | 1 |
Roles most likely to require distributed data processing
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| AI/ML Scientist | 3 | 13.6% |
| Machine Learning Research Engineer | 2 | 10.0% |
| Senior Data Engineer | 2 | 6.9% |
| Information Technology Specialist | 1 | 4.2% |
| Lead Data Engineer | 3 | 4.1% |
| Data Engineering Director | 2 | 3.0% |
| Technical Lead Manager | 1 | 2.9% |
| Applied Scientist | 7 | 2.8% |
| Chief Architect | 1 | 2.5% |
| Big Data Engineer | 1 | 2.4% |
Roles with the most distributed data processing postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Engineer | 111 | 40.4% |
| Software Engineer | 31 | 11.3% |
| Data Scientist | 27 | 9.8% |
| Machine Learning Engineer | 15 | 5.5% |
| Applied Scientist | 7 | 2.5% |
| Research Engineer | 5 | 1.8% |
| AI Engineer | 4 | 1.5% |
| Platform Engineer | 4 | 1.5% |
| AI/ML Scientist | 3 | 1.1% |
| Backend Engineer | 3 | 1.1% |
Top companies posting jobs requiring distributed data processing
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Mastercard | 13 |
| Netflix | 9 |
| Celonis | 8 |
| Barclays | 8 |
| Vanguard | 7 |
| Cisco | 7 |
| ASOS | 7 |
| General Motors | 6 |
| Zeta Global | 5 |
| Doximity | 5 |
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 distributed data processing
| Name | Postings | Share |
|---|---|---|
| London | 12 | 4.4% |
| New York City | 8 | 2.9% |
| San Francisco | 8 | 2.9% |
| Hyderabad | 7 | 2.5% |
| Bengaluru | 6 | 2.2% |
| Copenhagen | 5 | 1.8% |
| Pune | 5 | 1.8% |
| Seattle | 5 | 1.8% |
| Warsaw | 5 | 1.8% |
Skills commonly paired with distributed data processing
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 distributed data processing by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s distributed data processing postings by all distributed data processing 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
- a39e068217c09565
- data_as_of
- 2026-10-30
- window_days
- 90