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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
2026-08-17 00:00:00
2026-08-17 00:00:00
category
stringclasses
6 values
skill
stringlengths
2
20
skill_group
stringclasses
37 values
listing_count
int64
1
790
total_listings
int64
624
4.6k
demand_pct
float64
0
43.6
required_count
float64
0
740
⌀
2026-08-17
data
SQL
Language
457
2,589
17.7
null
2026-08-17
data
Python
Language
395
2,589
15.3
null
2026-08-17
data
Stakeholder Mgmt
Soft Skill
243
2,589
9.4
null
2026-08-17
data
Machine Learning
Skill
218
2,589
8.4
null
2026-08-17
data
Azure
Cloud
167
2,589
6.5
null
2026-08-17
data
Statistical Analysis
Skill
165
2,589
6.4
null
2026-08-17
data
AWS
Cloud
147
2,589
5.7
null
2026-08-17
data
A/B Testing
Skill
141
2,589
5.4
null
2026-08-17
data
Snowflake
Warehouse
137
2,589
5.3
null
2026-08-17
data
Git
Tool
137
2,589
5.3
null
2026-08-17
data
dbt
Transform
133
2,589
5.1
null
2026-08-17
data
Data Modeling
Skill
129
2,589
5
null
2026-08-17
data
Databricks
Platform
125
2,589
4.8
null
2026-08-17
data
CI/CD
Pipeline
123
2,589
4.8
null
2026-08-17
data
Spark
Processing
123
2,589
4.8
null
2026-08-17
data
LLMs / GenAI
Skill
119
2,589
4.6
null
2026-08-17
data
ETL
Skill
111
2,589
4.3
null
2026-08-17
data
Airflow
Orchestrator
105
2,589
4.1
null
2026-08-17
data
AI Agents
Technique
91
2,589
3.5
null
2026-08-17
data
Power BI
BI
87
2,589
3.4
null
2026-08-17
data
BigQuery
Warehouse
80
2,589
3.1
null
2026-08-17
data
GCP
Cloud
81
2,589
3.1
null
2026-08-17
data
Tableau
BI
79
2,589
3.1
null
2026-08-17
data
Looker
BI
70
2,589
2.7
null
2026-08-17
data
Excel
Tool
66
2,589
2.5
null
2026-08-17
data
Kafka
Streaming
56
2,589
2.2
null
2026-08-17
data
Java
Language
53
2,589
2
null
2026-08-17
data
Data Visualization
Skill
50
2,589
1.9
null
2026-08-17
data
Salesforce
Platform
36
2,589
1.4
null
2026-08-17
data
Agile / Scrum
Methodology
36
2,589
1.4
null
2026-08-17
data
Scala
Language
36
2,589
1.4
null
2026-08-17
data
Terraform
IaC
34
2,589
1.3
null
2026-08-17
data
Kubernetes
Orchestration
33
2,589
1.3
null
2026-08-17
data
Data Pipeline
Skill
31
2,589
1.2
null
2026-08-17
data
Redshift
Warehouse
32
2,589
1.2
null
2026-08-17
data
AI Coding Tools
Tool
28
2,589
1.1
null
2026-08-17
data
Microsoft Fabric
Platform
29
2,589
1.1
null
2026-08-17
data
Workday
Platform
25
2,589
1
null
2026-08-17
data
Flink
Streaming
26
2,589
1
null
2026-08-17
data
Docker
DevOps
26
2,589
1
null
2026-08-17
data
PostgreSQL
Database
27
2,589
1
null
2026-08-17
data
scikit-learn
Library
25
2,589
1
null
2026-08-17
data
Pandas
Library
27
2,589
1
null
2026-08-17
data
Apache Iceberg
Table Format
24
2,589
0.9
null
2026-08-17
data
NLP
Skill
24
2,589
0.9
null
2026-08-17
data
Dagster
Orchestrator
20
2,589
0.8
null
2026-08-17
data
PyTorch
Framework
21
2,589
0.8
null
2026-08-17
data
Deep Learning
Skill
22
2,589
0.8
null
2026-08-17
data
TensorFlow
Framework
18
2,589
0.7
null
2026-08-17
data
TypeScript
Language
17
2,589
0.7
null
2026-08-17
data
Trino
Query Engine
19
2,589
0.7
null
2026-08-17
data
NumPy
Library
15
2,589
0.6
null
2026-08-17
data
Prototyping
Skill
16
2,589
0.6
null
2026-08-17
data
MySQL
Database
15
2,589
0.6
null
2026-08-17
data
Segment
Analytics
15
2,589
0.6
null
2026-08-17
data
RAG
Technique
16
2,589
0.6
null
2026-08-17
data
Data Observability
Skill
14
2,589
0.5
null
2026-08-17
data
Fivetran
Tool
13
2,589
0.5
null
2026-08-17
data
SageMaker
MLOps
12
2,589
0.5
null
2026-08-17
data
LLM APIs
API
12
2,589
0.5
null
2026-08-17
data
JavaScript
Language
14
2,589
0.5
null
2026-08-17
data
ClickHouse
Database
14
2,589
0.5
null
2026-08-17
data
Metabase
BI
11
2,589
0.4
null
2026-08-17
data
SAP
Platform
11
2,589
0.4
null
2026-08-17
data
Datadog
Monitoring
10
2,589
0.4
null
2026-08-17
data
MongoDB
Database
11
2,589
0.4
null
2026-08-17
data
Jira
Tool
11
2,589
0.4
null
2026-08-17
data
DynamoDB
Database
11
2,589
0.4
null
2026-08-17
data
FastAPI
Framework
8
2,589
0.3
null
2026-08-17
data
Node.js
Runtime
9
2,589
0.3
null
2026-08-17
data
Spring
Framework
7
2,589
0.3
null
2026-08-17
data
Elasticsearch
Database
8
2,589
0.3
null
2026-08-17
data
Kotlin
Language
7
2,589
0.3
null
2026-08-17
data
Fine-tuning
Technique
8
2,589
0.3
null
2026-08-17
data
React
Framework
8
2,589
0.3
null
2026-08-17
data
Go
Language
9
2,589
0.3
null
2026-08-17
data
Prefect
Orchestrator
8
2,589
0.3
null
2026-08-17
data
Incident Response
Skill
8
2,589
0.3
null
2026-08-17
data
Linux
OS
7
2,589
0.3
null
2026-08-17
data
Google Analytics
Analytics
8
2,589
0.3
null
2026-08-17
data
System Design
Skill
8
2,589
0.3
null
2026-08-17
data
Mixpanel
Analytics
8
2,589
0.3
null
2026-08-17
data
C++
Language
7
2,589
0.3
null
2026-08-17
data
SIEM
Tool
9
2,589
0.3
null
2026-08-17
data
Rust
Language
7
2,589
0.3
null
2026-08-17
data
Superset
BI
7
2,589
0.3
null
2026-08-17
data
AWS Security
Cloud
6
2,589
0.2
null
2026-08-17
data
GraphQL
API
5
2,589
0.2
null
2026-08-17
data
ServiceNow
Platform
6
2,589
0.2
null
2026-08-17
data
SAS
Language
6
2,589
0.2
null
2026-08-17
data
Amplitude
Analytics
6
2,589
0.2
null
2026-08-17
data
SSIS
Tool
4
2,589
0.2
null
2026-08-17
data
LangChain
Framework
6
2,589
0.2
null
2026-08-17
data
MLflow
MLOps
5
2,589
0.2
null
2026-08-17
data
Hugging Face
Library
6
2,589
0.2
null
2026-08-17
data
C#
Language
5
2,589
0.2
null
2026-08-17
data
Vector Databases
Database
6
2,589
0.2
null
2026-08-17
data
Qlik
BI
4
2,589
0.2
null
2026-08-17
data
Prompt Engineering
Skill
6
2,589
0.2
null
2026-08-17
data
Figma
Design
4
2,589
0.2
null
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." 2026-08-17. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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