How we recognise a data engineer on GitHub
We count someone as a data engineer when their public work moves and models data at scale: pipelines, orchestration, streaming and warehouses.
In practice, their repositories, descriptions or bio carry at least two such signals: Spark, Airflow, dbt, Kafka or Flink, warehouses like Snowflake, BigQuery, Redshift or Databricks, orchestrators like Dagster or Prefect, ETL and ELT jobs. Analysis on top of that data is classified as data science.
The stack inside the role
The platform matters more than the language. Which processing, streaming and warehouse technologies appear in their work:
| # | Technology | Share of data engineers |
|---|---|---|
| 1 | Python | 36% |
| 2 | Kafka | 25% |
| 3 | Java | 15% |
| 4 | Elasticsearch | 14% |
| 5 | dbt | 13% |
| 6 | Databricks | 13% |
| 7 | TypeScript | 12% |
| 8 | Snowflake | 11% |
| 9 | AWS | 10% |
| 10 | RAG | 9% |
Which countries have the most data engineers?
Data platforms cluster where large companies centralise their data. The count gives the volume; the index shows the countries whose developers lean towards pipelines and warehouses.
| # | Country | Developers | Share of local developers | vs index |
|---|---|---|---|---|
| 1 | United States | 1,600+ | 2.8% | 1.1x |
| 2 | United Kingdom | 370+ | 2.7% | 1x |
| 3 | Germany | 340+ | 3.9% | 1.5x |
| 4 | India | 280+ | 3% | 1.2x |
| 5 | France | 220+ | 1.9% | 0.7x |
| 6 | Canada | 200+ | 3.7% | 1.4x |
| 7 | Brazil | 170+ | 3.2% | 1.2x |
| 8 | China | 160+ | 3.2% | 1.2x |
| 9 | Netherlands | 150+ | 5.9% | 2.3x |
| 10 | Spain | 130+ | 3.1% | 1.2x |
Min. 1,000 indexed developers and 30 data engineers per country; counts rounded down. Method: by-country study.
The Netherlands stands out: 5.9% of its indexed developers, 2.3x the index average.
Cities
| # | City | Share of located data engineers |
|---|---|---|
| 1 | San Francisco | 11% |
| 2 | London | 6% |
| 3 | New York | 4% |
| 4 | Seattle | 4% |
| 5 | Berlin | 3% |
| 6 | Paris | 3% |
| 7 | Amsterdam | 2% |
| 8 | Seoul | 2% |
Where the role overlaps with others
A profile can carry two roles. 28% of data engineers also qualify for infrastructure, DevOps and SRE: a spec asking for both describes a real, if narrower, profile.
| # | Also classified as | Share of data engineers |
|---|---|---|
| 1 | Software engineering | 42% |
| 2 | Infrastructure, DevOps and SRE | 28% |
| 3 | AI product engineering | 19% |
How reachable are they?
| Signal | Data engineers | Index average |
|---|---|---|
| Public email | 30% | 27% |
| Available for hire | 16% | 15% |
| Pushed code, last 90 days | 40% | 55% |
Name the platform. Data engineering splits by stack more than by language: a Spark and Databricks shop and a dbt and Snowflake shop hire from pools that overlap less than their job titles suggest.
Who employs data engineers today?
| # | Company |
|---|---|
| 1 | Databricks |
| 2 | elastic |
| 3 | Snowflake |
| 4 | IBM |
| 5 | Red Hat |
| 6 | AWS |
| 7 | Microsoft |
| 8 | Confluent |
What they build themselves
| # | Repository | Stars |
|---|---|---|
| 1 | CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | ★ 28,184 |
| 2 | rwaldron/idiomatic.js | ★ 25,728 |
| 3 | bevacqua/dragula | ★ 22,129 |
| 4 | lionsoul2014/ip2region | ★ 19,571 |
| 5 | deviantony/docker-elk | ★ 18,388 |
For the language side of the same search, see the guides to hiring Python developers and hiring Java developers. To write the query by hand first, use the GitHub user search builder.
Frequently asked questions
How many data engineers are on GitHub?
At least 8,600: that is how many StarHunt's index places in the role as of October 2026, counting only work visible in public repositories.
Should a data engineer job ask for Python or for Spark?
For the platform. Most data engineers write Python or SQL; what separates candidates is whether they have run the processing, streaming and warehouse tools you use. Asking for the language alone returns most of the index.
Where are data engineers concentrated?
San Francisco is the largest hub among those we can place on a map, and the Netherlands has the highest concentration relative to its size.
Name the stack, Spark, dbt, Kafka, Snowflake, and get data engineers who run it in public, with the contact details they chose to publish.
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