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snapshot_date
stringdate
2026-08-05 00:00:00
2026-08-05 00:00:00
category
stringclasses
6 values
skill
stringlengths
1
20
skill_group
stringclasses
37 values
listing_count
int64
1
1.37k
total_listings
int64
2.35k
13.7k
demand_pct
float64
0
33.3
required_count
int64
0
1.35k
2026-08-05
data
SQL
Language
1,057
9,040
11.7
1,047
2026-08-05
data
Python
Language
874
9,040
9.7
852
2026-08-05
data
Machine Learning
Skill
614
9,040
6.8
607
2026-08-05
data
Stakeholder Mgmt
Soft Skill
511
9,040
5.7
502
2026-08-05
data
AWS
Cloud
392
9,040
4.3
376
2026-08-05
data
ETL
Skill
334
9,040
3.7
326
2026-08-05
data
Power BI
BI
312
9,040
3.5
307
2026-08-05
data
Statistical Analysis
Skill
309
9,040
3.4
294
2026-08-05
data
Data Modeling
Skill
301
9,040
3.3
292
2026-08-05
data
Databricks
Platform
295
9,040
3.3
283
2026-08-05
data
Snowflake
Warehouse
290
9,040
3.2
276
2026-08-05
data
Azure
Cloud
291
9,040
3.2
286
2026-08-05
data
Spark
Processing
253
9,040
2.8
239
2026-08-05
data
LLMs / GenAI
Skill
222
9,040
2.5
213
2026-08-05
data
A/B Testing
Skill
209
9,040
2.3
199
2026-08-05
data
dbt
Transform
189
9,040
2.1
167
2026-08-05
data
Excel
Tool
177
9,040
2
170
2026-08-05
data
Tableau
BI
166
9,040
1.8
156
2026-08-05
data
Airflow
Orchestrator
143
9,040
1.6
117
2026-08-05
data
GCP
Cloud
149
9,040
1.6
144
2026-08-05
data
Agile / Scrum
Methodology
145
9,040
1.6
141
2026-08-05
data
Git
Tool
130
9,040
1.4
124
2026-08-05
data
CI/CD
Pipeline
113
9,040
1.3
108
2026-08-05
data
BigQuery
Warehouse
105
9,040
1.2
91
2026-08-05
data
Java
Language
103
9,040
1.1
101
2026-08-05
data
AI Agents
Technique
101
9,040
1.1
100
2026-08-05
data
Looker
BI
91
9,040
1
81
2026-08-05
data
NLP
Skill
77
9,040
0.9
75
2026-08-05
data
Data Visualization
Skill
84
9,040
0.9
82
2026-08-05
data
Kafka
Streaming
75
9,040
0.8
63
2026-08-05
data
Data Pipeline
Skill
69
9,040
0.8
63
2026-08-05
data
Redshift
Warehouse
59
9,040
0.7
57
2026-08-05
data
Scala
Language
61
9,040
0.7
56
2026-08-05
data
Kubernetes
Orchestration
51
9,040
0.6
38
2026-08-05
data
Pandas
Library
56
9,040
0.6
55
2026-08-05
data
Terraform
IaC
58
9,040
0.6
49
2026-08-05
data
Salesforce
Platform
53
9,040
0.6
47
2026-08-05
data
Microsoft Fabric
Platform
54
9,040
0.6
54
2026-08-05
data
RAG
Technique
43
9,040
0.5
41
2026-08-05
data
Docker
DevOps
47
9,040
0.5
37
2026-08-05
data
PostgreSQL
Database
44
9,040
0.5
44
2026-08-05
data
NumPy
Library
32
9,040
0.4
32
2026-08-05
data
PyTorch
Framework
36
9,040
0.4
36
2026-08-05
data
scikit-learn
Library
37
9,040
0.4
37
2026-08-05
data
Workday
Platform
29
9,040
0.3
29
2026-08-05
data
Apache Iceberg
Table Format
25
9,040
0.3
21
2026-08-05
data
MySQL
Database
24
9,040
0.3
24
2026-08-05
data
AI Coding Tools
Tool
25
9,040
0.3
24
2026-08-05
data
Jira
Tool
28
9,040
0.3
27
2026-08-05
data
TensorFlow
Framework
28
9,040
0.3
28
2026-08-05
data
Deep Learning
Skill
28
9,040
0.3
28
2026-08-05
data
Prototyping
Skill
30
9,040
0.3
29
2026-08-05
data
Flink
Streaming
29
9,040
0.3
25
2026-08-05
data
Linux
OS
20
9,040
0.2
16
2026-08-05
data
Segment
Analytics
19
9,040
0.2
17
2026-08-05
data
JavaScript
Language
22
9,040
0.2
20
2026-08-05
data
Dagster
Orchestrator
22
9,040
0.2
20
2026-08-05
data
Elasticsearch
Database
21
9,040
0.2
17
2026-08-05
data
DynamoDB
Database
14
9,040
0.2
14
2026-08-05
data
Data Observability
Skill
16
9,040
0.2
13
2026-08-05
data
SageMaker
MLOps
16
9,040
0.2
11
2026-08-05
data
Node.js
Runtime
17
9,040
0.2
17
2026-08-05
data
Fivetran
Tool
18
9,040
0.2
17
2026-08-05
data
AWS Security
Cloud
22
9,040
0.2
19
2026-08-05
data
SAP
Platform
20
9,040
0.2
20
2026-08-05
data
Trino
Query Engine
21
9,040
0.2
20
2026-08-05
data
SAS
Language
14
9,040
0.2
14
2026-08-05
data
MongoDB
Database
20
9,040
0.2
19
2026-08-05
data
TypeScript
Language
20
9,040
0.2
16
2026-08-05
data
Hugging Face
Library
11
9,040
0.1
11
2026-08-05
data
Airbyte
Tool
7
9,040
0.1
6
2026-08-05
data
Amplitude
Analytics
11
9,040
0.1
9
2026-08-05
data
C#
Language
11
9,040
0.1
11
2026-08-05
data
C++
Language
11
9,040
0.1
11
2026-08-05
data
ClickHouse
Database
13
9,040
0.1
12
2026-08-05
data
Datadog
Monitoring
9
9,040
0.1
9
2026-08-05
data
Dynamics 365
Platform
8
9,040
0.1
8
2026-08-05
data
FastAPI
Framework
8
9,040
0.1
8
2026-08-05
data
Figma
Design
7
9,040
0.1
7
2026-08-05
data
Fine-tuning
Technique
13
9,040
0.1
13
2026-08-05
data
Go
Language
9
9,040
0.1
9
2026-08-05
data
Google Analytics
Analytics
12
9,040
0.1
11
2026-08-05
data
Grafana
Monitoring
9
9,040
0.1
7
2026-08-05
data
GraphQL
API
6
9,040
0.1
5
2026-08-05
data
Incident Response
Skill
9
9,040
0.1
9
2026-08-05
data
Informatica
Tool
12
9,040
0.1
11
2026-08-05
data
Jupyter
Tool
5
9,040
0.1
5
2026-08-05
data
Kotlin
Language
10
9,040
0.1
10
2026-08-05
data
LangChain
Framework
7
9,040
0.1
7
2026-08-05
data
Linear
Tool
6
9,040
0.1
5
2026-08-05
data
LLM APIs
API
8
9,040
0.1
6
2026-08-05
data
Metabase
BI
13
9,040
0.1
12
2026-08-05
data
Mixpanel
Analytics
10
9,040
0.1
7
2026-08-05
data
MLflow
MLOps
9
9,040
0.1
7
2026-08-05
data
NetSuite
Platform
6
9,040
0.1
5
2026-08-05
data
Polars
Library
7
9,040
0.1
6
2026-08-05
data
Prefect
Orchestrator
12
9,040
0.1
12
2026-08-05
data
Prompt Engineering
Skill
7
9,040
0.1
6
2026-08-05
data
Qlik
BI
9
9,040
0.1
9
2026-08-05
data
React
Framework
9
9,040
0.1
8
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." 2026-08-05. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.

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