Spark: market
Apache Spark is the main engine for distributed big-data processing: batch pipelines, streaming, SQL over the data lake and ML feature preparation. We track 2 379 open positions listing Spark, 2 248 of them fresh. Spark is most often required of data engineers through PySpark; Databricks and cloud services (EMR, Dataproc, Synapse) made it the standard of lakehouse platforms. Most of the demand comes from Data, ML/AI and Backend roles. It most often appears next to Python, SQL and Databricks. 36% of the postings allow fully remote work. Below is measured Spark demand.
The Spark market currently has 2,379 open roles, 2,248 of them freshly observed. Median salary $7,125/mo. Observed candidate pool — 434.
36% of Spark postings allow fully remote work; the share counts only postings that state the work format.
⚠ salary known for 173 of 2379 jobs; remote share from 1910 with a stated format; 2248 counted as fresh observations; trend and hiring difficulty are not shown
Demand and observed supply
| Open demand | 2,379 |
| Observed supply | 434 |
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Salary distribution
One in ten earns under $4,157, one in ten over $17,600. Half the market falls between $5,787 and $13,068. Sample: 173.
The range histogram for Spark postings uses only postings with an explicit compensation range; the median is computed over the same sample.
Demand geography
| country | jobs |
|---|---|
| US | 729 |
| PL | 314 |
| IN | 184 |
| GB | 141 |
| DE | 69 |
| CA | 63 |
| RU | 60 |
| BR | 40 |
| HK | 30 |
| NL | 30 |
The top country for Spark demand is the US (729 positions).
⚠ job counts only: salary by country is not published
Used together with
Spark most often appears next to Python, SQL and Databricks. Databricks among the neighbours is the most common environment Spark runs in today.
Roles that use it
| role | jobs |
|---|---|
| Data | 1,282 |
| ML/AI | 271 |
| Backend | 109 |
| DevOps | 73 |
| Architect | 61 |
| Fullstack | 58 |
| AI Engineer | 46 |
| Analytics Engineer | 45 |
| Platform Engineer | 35 |
| QA | 32 |
⚠ the `Other` and `IT` buckets are excluded as non-roles
Demand by grade
| grade | jobs |
|---|---|
| senior | 783 |
| principal | 171 |
| lead | 157 |
| middle | 111 |
| junior | 53 |
Senior-and-above is about 87% of 1 275 graded Spark postings.
⚠ demand side only: the grade of the observed pool is unknown for most of it
Employers
Demand is spread across 956 employers. The largest accounts for 2.7%, the top ten for 14.8%; the remaining 85.2% is long tail.
| share | value |
|---|---|
| top-1 | 2.7% |
| top-3 | 6.3% |
| top-10 | 14.8% |
| long tail | 85.2% |
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Where Zorky sees this market
Observed across 102 sources; the largest accounts for 28.3% — this market does not rest on a single channel.
Spark postings arrive mostly from ATS & career sites and job boards & aggregators.
Recent openings
- Contract Data Engineer · HK
- Data Engineer · PL · $6,500
- Azure Platform Architect · PL
- Data Engineer (Mid Level) · IN
- Data Architect · US
- Senior Software Engineer · US
- Lead Solutions Architect – MedTech (Strategic Account) · NL
- Full Stack / Big Data Engineer Agentic AI · US
Adjacent markets
⚠ adjacent markets for comparison are not defined yet
How this is measured
- Vacancy
- an open job that cleared the quality gate and lists at least two technologies
- Observed candidate
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Matchable candidate
- not counted yet
- Window
- jobs open at the moment the snapshot was built
About the data
- Some breakdowns are hidden: their data coverage is not yet sufficient.
- Statistics are shown only where the sample clears a quality gate.
- A missing block does not mean a value of zero.
Breakdowns currently hidden: 7.
Data as of 2026-10-20
Frequently asked questions
Common questions about the Spark market — short answers from the same data slice.
What is asked about Spark in a data-engineer interview?
Lazy evaluation and execution plans, partitioning and shuffle, broadcast joins, handling data skew, caching, storage formats (Parquet, Delta), Structured Streaming.
PySpark or Scala for Spark?
PySpark is noticeably more common on the market — it is part of the standard data-engineer toolkit. Scala appears in teams where performance matters and custom extensions are written.
How big is the Spark market?
The slice tracks 2 379 open positions listing Spark; Spark rarely appears in the job title — it is part of the data-engineer, ML-engineer and data-architect profile.