Datasets:

datamatastudios
/

skill-demand-index

snapshot_date stringdate 2026-10-07 00:00:00 2026-10-07 00:00:00	category stringclasses 6 values	skill stringlengths 1 20	skill_group stringclasses 37 values	listing_count int64 1 1.38k	total_listings int64 1.28k 8.37k	demand_pct float64 0 37.4	required_count int64 0 1.29k
2026-10-07	data	SQL	Language	975	5,035	19.4	968
2026-10-07	data	Python	Language	814	5,035	16.2	783
2026-10-07	data	Machine Learning	Skill	484	5,035	9.6	472
2026-10-07	data	Stakeholder Mgmt	Soft Skill	458	5,035	9.1	453
2026-10-07	data	AWS	Cloud	322	5,035	6.4	298
2026-10-07	data	ETL	Skill	314	5,035	6.2	306
2026-10-07	data	Databricks	Platform	308	5,035	6.1	299
2026-10-07	data	Spark	Processing	300	5,035	6	280
2026-10-07	data	Azure	Cloud	302	5,035	6	293
2026-10-07	data	Data Modeling	Skill	289	5,035	5.7	278
2026-10-07	data	Statistical Analysis	Skill	271	5,035	5.4	254
2026-10-07	data	Power BI	BI	248	5,035	4.9	246
2026-10-07	data	Snowflake	Warehouse	238	5,035	4.7	223
2026-10-07	data	dbt	Transform	239	5,035	4.7	214
2026-10-07	data	LLMs / GenAI	Skill	219	5,035	4.3	203
2026-10-07	data	A/B Testing	Skill	215	5,035	4.3	207
2026-10-07	data	Git	Tool	199	5,035	4	179
2026-10-07	data	AI Agents	Technique	185	5,035	3.7	176
2026-10-07	data	Airflow	Orchestrator	181	5,035	3.6	157
2026-10-07	data	CI/CD	Pipeline	164	5,035	3.3	148
2026-10-07	data	GCP	Cloud	155	5,035	3.1	143
2026-10-07	data	Tableau	BI	152	5,035	3	141
2026-10-07	data	Agile / Scrum	Methodology	143	5,035	2.8	135
2026-10-07	data	BigQuery	Warehouse	134	5,035	2.7	122
2026-10-07	data	Excel	Tool	124	5,035	2.5	124
2026-10-07	data	Looker	BI	108	5,035	2.1	94
2026-10-07	data	Kafka	Streaming	103	5,035	2	88
2026-10-07	data	Microsoft Fabric	Platform	101	5,035	2	101
2026-10-07	data	Data Visualization	Skill	82	5,035	1.6	78
2026-10-07	data	Java	Language	74	5,035	1.5	70
2026-10-07	data	Kubernetes	Orchestration	77	5,035	1.5	66
2026-10-07	data	Scala	Language	68	5,035	1.4	63
2026-10-07	data	Terraform	IaC	72	5,035	1.4	63
2026-10-07	data	Data Pipeline	Skill	61	5,035	1.2	56
2026-10-07	data	Redshift	Warehouse	62	5,035	1.2	61
2026-10-07	data	Salesforce	Platform	57	5,035	1.1	53
2026-10-07	data	Docker	DevOps	54	5,035	1.1	46
2026-10-07	data	Flink	Streaming	44	5,035	0.9	38
2026-10-07	data	Pandas	Library	46	5,035	0.9	41
2026-10-07	data	PostgreSQL	Database	42	5,035	0.8	39
2026-10-07	data	RAG	Technique	39	5,035	0.8	37
2026-10-07	data	Apache Iceberg	Table Format	40	5,035	0.8	37
2026-10-07	data	NLP	Skill	41	5,035	0.8	39
2026-10-07	data	scikit-learn	Library	35	5,035	0.7	32
2026-10-07	data	Dagster	Orchestrator	35	5,035	0.7	32
2026-10-07	data	AI Coding Tools	Tool	33	5,035	0.7	26
2026-10-07	data	Prototyping	Skill	29	5,035	0.6	26
2026-10-07	data	PyTorch	Framework	31	5,035	0.6	30
2026-10-07	data	Fivetran	Tool	32	5,035	0.6	28
2026-10-07	data	Trino	Query Engine	32	5,035	0.6	29
2026-10-07	data	TypeScript	Language	28	5,035	0.6	24
2026-10-07	data	SAP	Platform	29	5,035	0.6	28
2026-10-07	data	Incident Response	Skill	28	5,035	0.6	27
2026-10-07	data	Segment	Analytics	23	5,035	0.5	22
2026-10-07	data	Deep Learning	Skill	27	5,035	0.5	26
2026-10-07	data	Jira	Tool	24	5,035	0.5	20
2026-10-07	data	Workday	Platform	26	5,035	0.5	25
2026-10-07	data	NumPy	Library	25	5,035	0.5	23
2026-10-07	data	JavaScript	Language	18	5,035	0.4	18
2026-10-07	data	Linux	OS	19	5,035	0.4	17
2026-10-07	data	LLM APIs	API	19	5,035	0.4	17
2026-10-07	data	C++	Language	20	5,035	0.4	19
2026-10-07	data	ClickHouse	Database	18	5,035	0.4	14
2026-10-07	data	TensorFlow	Framework	21	5,035	0.4	21
2026-10-07	data	Metabase	BI	20	5,035	0.4	19
2026-10-07	data	System Design	Skill	20	5,035	0.4	20
2026-10-07	data	MLflow	MLOps	20	5,035	0.4	11
2026-10-07	data	Datadog	Monitoring	14	5,035	0.3	13
2026-10-07	data	AWS Security	Cloud	15	5,035	0.3	15
2026-10-07	data	SageMaker	MLOps	16	5,035	0.3	13
2026-10-07	data	SAS	Language	14	5,035	0.3	14
2026-10-07	data	Workflow Automation	Tool	17	5,035	0.3	17
2026-10-07	data	React	Framework	15	5,035	0.3	15
2026-10-07	data	Data Observability	Skill	13	5,035	0.3	9
2026-10-07	data	Prefect	Orchestrator	15	5,035	0.3	15
2026-10-07	data	Prompt Engineering	Skill	15	5,035	0.3	14
2026-10-07	data	Fine-tuning	Technique	17	5,035	0.3	15
2026-10-07	data	Informatica	Tool	14	5,035	0.3	13
2026-10-07	data	Elasticsearch	Database	15	5,035	0.3	10
2026-10-07	data	XGBoost	Library	8	5,035	0.2	6
2026-10-07	data	Airbyte	Tool	10	5,035	0.2	10
2026-10-07	data	Amplitude	Analytics	9	5,035	0.2	8
2026-10-07	data	Bash	Language	8	5,035	0.2	7
2026-10-07	data	DynamoDB	Database	12	5,035	0.2	12
2026-10-07	data	Go	Language	9	5,035	0.2	7
2026-10-07	data	Google Analytics	Analytics	11	5,035	0.2	9
2026-10-07	data	Grafana	Monitoring	10	5,035	0.2	6
2026-10-07	data	Helm	Orchestration	12	5,035	0.2	9
2026-10-07	data	Kotlin	Language	10	5,035	0.2	7
2026-10-07	data	LangChain	Framework	11	5,035	0.2	11
2026-10-07	data	Mixpanel	Analytics	10	5,035	0.2	9
2026-10-07	data	MongoDB	Database	11	5,035	0.2	11
2026-10-07	data	MySQL	Database	10	5,035	0.2	10
2026-10-07	data	Oracle	Database	12	5,035	0.2	12
2026-10-07	data	Redis	Database	8	5,035	0.2	8
2026-10-07	data	REST API	API	8	5,035	0.2	7
2026-10-07	data	Ruby	Language	9	5,035	0.2	9
2026-10-07	data	Rust	Language	11	5,035	0.2	11
2026-10-07	data	ServiceNow	Platform	8	5,035	0.2	8
2026-10-07	data	SIEM	Tool	8	5,035	0.2	6

End of preview. Expand in Data Studio

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.

Latest snapshot: 2026-10-07
Rows in this release: 739
Updated: daily
Licence: CC BY 4.0 — free to use and adapt, including commercially, with attribution.
Source & methodology: https://www.datamatastudios.com/datasets

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." 2026-10-07. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.

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