large-scale data processing jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-15, large-scale data processing appears in 138 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning large-scale data processing, with demand share down 10.0% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-15
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Frequently asked questions about large-scale data processing
+Is large-scale data processing in demand in 2026?
Yes. large-scale data processing appears in 138 job postings indexed by Skillenai over the 90 days ending 2026-10-15. Software Engineer accounts for the most postings mentioning large-scale data processing (26.8% of all postings mentioning large-scale data processing).
+What jobs require large-scale data processing?
According to the Skillenai jobs index over the 90 days ending 2026-10-15, among roles with at least 20 postings, the highest shares mentioning large-scale data processing are Large Language Model Architect (21.7% of that role’s postings mention large-scale data processing), Core Infrastructure Engineer (7.3% of that role’s postings mention large-scale data processing), ML Software Engineer (5.0% of that role’s postings mention large-scale data processing).
+What skills are commonly paired with large-scale data processing?
Across job postings indexed by Skillenai (90 days ending 2026-10-15), large-scale data processing most often appears alongside Python, Distributed systems, machine learning, data pipelines, PyTorch.
+Where is large-scale data processing most in demand?
As of 2026-10-15, the metro areas posting the most jobs requiring large-scale data processing are San Francisco, Mountain View, New York City, Austin, Helsinki, according to the Skillenai jobs index.
+How can I keep up with new large-scale data processing content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning large-scale data processing alongside the jobs index. You can subscribe to a daily email digest of new large-scale data processing content from your Skillenai account.
+Which skills come before and after large-scale 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 large-scale data processing — last 90 days
Career paths around large-scale data processing
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before large-scale data processing
No published incoming skill pairs yet.
Skills after large-scale 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 |
|---|---|
| After: Data quality | 1 |
| After: data storage | 1 |
| After: data governance frameworks | 1 |
| After: user experience | 1 |
| After: data security | 1 |
| After: data processing | 1 |
| After: AI algorithms | 1 |
| After: machine learning models | 1 |
Roles most likely to require large-scale data processing
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Large Language Model Architect | 5 | 21.7% |
| Core Infrastructure Engineer | 3 | 7.3% |
| ML Software Engineer | 1 | 5.0% |
| ML Engineering Manager | 1 | 4.2% |
| Research Program Manager | 1 | 3.8% |
| ETL Developer | 2 | 3.6% |
| Machine Learning Infrastructure Engineer | 1 | 3.4% |
| Product Architect | 1 | 3.4% |
| Applied AI Scientist | 1 | 3.0% |
| Data Infrastructure Engineer | 1 | 2.9% |
Roles with the most large-scale data processing postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 37 | 26.8% |
| Machine Learning Engineer | 9 | 6.5% |
| Data Scientist | 5 | 3.6% |
| Large Language Model Architect | 5 | 3.6% |
| AI Engineer | 4 | 2.9% |
| Data Engineer | 4 | 2.9% |
| Fullstack Engineer | 4 | 2.9% |
| Research Engineer | 4 | 2.9% |
| Applied Scientist | 3 | 2.2% |
| Core Infrastructure Engineer | 3 | 2.2% |
Top companies posting jobs requiring large-scale data processing
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Waymo | 9 |
| Accenture | 8 |
| 6 | |
| Anthropic | 6 |
| Scale AI | 5 |
| Renesas | 4 |
| Smartly | 4 |
| PhaseV | 3 |
| XPeng | 3 |
| Visa | 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 large-scale data processing
| Name | Postings | Share |
|---|---|---|
| San Francisco | 12 | 8.7% |
| Mountain View | 7 | 5.1% |
| New York City | 5 | 3.6% |
| Austin | 4 | 2.9% |
| Helsinki | 4 | 2.9% |
| Michigan City | 4 | 2.9% |
| Bengaluru | 3 | 2.2% |
| Santa Clara | 3 | 2.2% |
| Bellevue | 2 | 1.4% |
Skills commonly paired with large-scale data processing
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-15. 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 large-scale 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 large-scale data processing postings by all large-scale 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 2,614 identified employers and covers 71% of earlier and 72% of latest indexed postings. Windows: 2026-06-16 to 2026-09-14 and 2026-07-14 to 2026-10-12 (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
- 19df7aa9e2532072
- data_as_of
- 2026-10-15
- 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.
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