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Datasets:
datamatastudios
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skill-demand-index

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Tags:
job-market
tech-skills
hiring-trends
labor-market
skills
jobs
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snapshot_date
stringdate
2026-07-02 00:00:00
2026-07-02 00:00:00
category
stringclasses
6 values
skill
stringlengths
1
20
skill_group
stringclasses
34 values
listing_count
int64
1
634
total_listings
int64
791
4.77k
demand_pct
float64
0
33.5
required_count
int64
0
615
2026-07-02
data
SQL
Language
412
2,905
14.2
405
2026-07-02
data
Python
Language
338
2,905
11.6
323
2026-07-02
data
Machine Learning
Skill
261
2,905
9
257
2026-07-02
data
Stakeholder Mgmt
Soft Skill
238
2,905
8.2
234
2026-07-02
data
Statistical Analysis
Skill
135
2,905
4.6
128
2026-07-02
data
AWS
Cloud
135
2,905
4.6
126
2026-07-02
data
Spark
Processing
122
2,905
4.2
113
2026-07-02
data
ETL
Skill
123
2,905
4.2
117
2026-07-02
data
Data Modeling
Skill
123
2,905
4.2
117
2026-07-02
data
Snowflake
Warehouse
120
2,905
4.1
113
2026-07-02
data
A/B Testing
Skill
113
2,905
3.9
106
2026-07-02
data
Azure
Cloud
104
2,905
3.6
100
2026-07-02
data
LLMs / GenAI
Skill
99
2,905
3.4
97
2026-07-02
data
Databricks
Platform
95
2,905
3.3
88
2026-07-02
data
Power BI
BI
90
2,905
3.1
89
2026-07-02
data
dbt
Transform
68
2,905
2.3
54
2026-07-02
data
Airflow
Orchestrator
64
2,905
2.2
52
2026-07-02
data
Tableau
BI
62
2,905
2.1
58
2026-07-02
data
Excel
Tool
58
2,905
2
56
2026-07-02
data
GCP
Cloud
44
2,905
1.5
41
2026-07-02
data
Agile / Scrum
Methodology
45
2,905
1.5
43
2026-07-02
data
Git
Tool
42
2,905
1.4
40
2026-07-02
data
Java
Language
40
2,905
1.4
40
2026-07-02
data
CI/CD
Pipeline
39
2,905
1.3
37
2026-07-02
data
Data Visualization
Skill
39
2,905
1.3
37
2026-07-02
data
Looker
BI
38
2,905
1.3
34
2026-07-02
data
NLP
Skill
34
2,905
1.2
32
2026-07-02
data
Kafka
Streaming
34
2,905
1.2
28
2026-07-02
data
BigQuery
Warehouse
32
2,905
1.1
26
2026-07-02
data
Data Pipeline
Skill
33
2,905
1.1
29
2026-07-02
data
Scala
Language
28
2,905
1
24
2026-07-02
data
Pandas
Library
25
2,905
0.9
24
2026-07-02
data
Redshift
Warehouse
27
2,905
0.9
27
2026-07-02
data
Prototyping
Skill
23
2,905
0.8
23
2026-07-02
data
Kubernetes
Orchestration
23
2,905
0.8
18
2026-07-02
data
Terraform
IaC
23
2,905
0.8
19
2026-07-02
data
PostgreSQL
Database
17
2,905
0.6
17
2026-07-02
data
Flink
Streaming
17
2,905
0.6
15
2026-07-02
data
PyTorch
Framework
16
2,905
0.6
14
2026-07-02
data
scikit-learn
Library
18
2,905
0.6
16
2026-07-02
data
Docker
DevOps
14
2,905
0.5
12
2026-07-02
data
RAG
Technique
15
2,905
0.5
14
2026-07-02
data
Elasticsearch
Database
11
2,905
0.4
8
2026-07-02
data
Deep Learning
Skill
13
2,905
0.4
13
2026-07-02
data
Dagster
Orchestrator
11
2,905
0.4
10
2026-07-02
data
NumPy
Library
12
2,905
0.4
12
2026-07-02
data
MLflow
MLOps
12
2,905
0.4
12
2026-07-02
data
Segment
Analytics
10
2,905
0.3
9
2026-07-02
data
MySQL
Database
8
2,905
0.3
8
2026-07-02
data
AWS Security
Cloud
8
2,905
0.3
5
2026-07-02
data
TensorFlow
Framework
10
2,905
0.3
10
2026-07-02
data
System Design
Skill
9
2,905
0.3
9
2026-07-02
data
DynamoDB
Database
8
2,905
0.3
8
2026-07-02
data
Jira
Tool
9
2,905
0.3
9
2026-07-02
data
Hugging Face
Library
5
2,905
0.2
5
2026-07-02
data
Amplitude
Analytics
5
2,905
0.2
5
2026-07-02
data
C++
Language
6
2,905
0.2
6
2026-07-02
data
Figma
Design
6
2,905
0.2
6
2026-07-02
data
Jupyter
Tool
5
2,905
0.2
5
2026-07-02
data
Kotlin
Language
5
2,905
0.2
5
2026-07-02
data
Linux
OS
7
2,905
0.2
6
2026-07-02
data
Node.js
Runtime
7
2,905
0.2
7
2026-07-02
data
React
Framework
7
2,905
0.2
7
2026-07-02
data
Rust
Language
5
2,905
0.2
5
2026-07-02
data
SageMaker
MLOps
5
2,905
0.2
5
2026-07-02
data
TypeScript
Language
6
2,905
0.2
6
2026-07-02
data
Prefect
Orchestrator
4
2,905
0.1
4
2026-07-02
data
REST API
API
2
2,905
0.1
1
2026-07-02
data
User Research
Skill
3
2,905
0.1
3
2026-07-02
data
SAS
Language
2
2,905
0.1
2
2026-07-02
data
SIEM
Tool
2
2,905
0.1
2
2026-07-02
data
Go
Language
2
2,905
0.1
2
2026-07-02
data
SOC 2
Compliance
3
2,905
0.1
3
2026-07-02
data
Flask
Framework
4
2,905
0.1
2
2026-07-02
data
Fivetran
Tool
4
2,905
0.1
3
2026-07-02
data
Fine-tuning
Technique
4
2,905
0.1
4
2026-07-02
data
Superset
BI
3
2,905
0.1
2
2026-07-02
data
Datadog
Monitoring
2
2,905
0.1
2
2026-07-02
data
C#
Language
4
2,905
0.1
3
2026-07-02
data
Angular
Framework
2
2,905
0.1
2
2026-07-02
data
MongoDB
Database
3
2,905
0.1
3
2026-07-02
data
Linear
Tool
2
2,905
0.1
2
2026-07-02
data
LangChain
Framework
2
2,905
0.1
2
2026-07-02
data
JavaScript
Language
4
2,905
0.1
4
2026-07-02
data
XGBoost
Library
2
2,905
0.1
2
2026-07-02
data
Incident Response
Skill
3
2,905
0.1
3
2026-07-02
data
Metabase
BI
1
2,905
0
1
2026-07-02
data
Grafana
Monitoring
1
2,905
0
1
2026-07-02
data
Google Analytics
Analytics
1
2,905
0
1
2026-07-02
data
R
Language
1
2,905
0
1
2026-07-02
data
FastAPI
Framework
1
2,905
0
1
2026-07-02
data
Microservices
Architecture
1
2,905
0
0
2026-07-02
data
Redis
Database
1
2,905
0
1
2026-07-02
data
Swift
Language
1
2,905
0
1
2026-07-02
data
PHP
Language
1
2,905
0
1
2026-07-02
data
Ruby
Language
1
2,905
0
1
2026-07-02
data
Polars
Library
1
2,905
0
0
2026-07-02
data
Vue
Framework
1
2,905
0
1
2026-07-02
data
Bash
Language
1
2,905
0
1
2026-07-02
data
Helm
Orchestration
1
2,905
0
1
End of preview. Expand in Data Studio

Datamata Skill Demand Index

Datamata Skill Demand Index

Daily share of active tech job listings mentioning each skill, across data, engineering, product, DevOps, security and AI. One row per category and skill from the most recent snapshot, including how often each skill is a hard requirement.

Quickstart 

import pandas as pd

# Stream straight from the Hub — no download step needed
df = pd.read_csv("hf://datasets/datamatastudios/skill-demand-index/skill-demand-index.csv")

# Highest-demand skills right now
print(df.sort_values("demand_pct", ascending=False).head(10))

Or load it with the 🤗 datasets library:

from datasets import load_dataset

ds = load_dataset("datamatastudios/skill-demand-index")

What you can answer with it

  • Which skills lead demand in data, engineering, product, DevOps, security or AI — and by how much.
  • How often a skill is a hard requirement versus nice-to-have (required_count vs listing_count).
  • How a skill's demand share moves over time, by appending each daily snapshot.

Columns

Column Type Description
snapshot_date string UTC date the snapshot was computed (YYYY-MM-DD).
category string Role category: data, engineering, product, devops, security or ai.
skill string Normalised skill name.
skill_group string Skill family the skill belongs to (e.g. language, cloud, framework).
listing_count number Active listings in the category that mention the skill.
total_listings number Total active listings in the category on that date.
demand_pct number listing_count / total_listings x 100, rounded to 0.1.
required_count number Listings where the skill is a hard requirement (vs nice-to-have). Blank for rows snapshotted before this was tracked.

How it is built

Active tech job listings are scraped daily from public applicant-tracking systems (Greenhouse, Lever, Ashby) and aggregated boards. For each role category the demand share of a skill is listings_with_skill / total_active_listings x 100. This release is the most recent daily snapshot for all six categories. Full method and known limitations: https://www.datamatastudios.com/methodology.

Citation

Datamata Studios. "Datamata Skill Demand Index." 2026-07-02. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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