# Databricks — Hiring Intelligence

> Generated: 2026-04-26 · Source: <https://yubhub.co/companies/databricks> · API: <https://api.yubhub.co/stats/company/databricks>

## Profile

- **Industry:** software industry
- **HQ:** San Francisco
- **Headcount:** 4,000
- **Website:** <http://databricks.com/>

## Hiring trajectory

- **Total enriched roles:** 508
- **26-week trend:** stable (week-over-week -91%)
- **14-day momentum:** 508 recent vs 0 prior · stable (+0%)
- **Market share:** 42.6% of industry (1,193 jobs across 2 companies) — rank 2

## Where the hiring is

| Category | Postings |
| --- | ---: |
| engineering | 367 |
| sales | 111 |
| marketing | 10 |
| legal | 5 |
| finance | 5 |
| operations | 4 |
| hr | 3 |
| design | 2 |

## Geographic distribution

| Region | Postings | Share |
| --- | ---: | ---: |
| United States | 227 | 44.7% |
| EU | 54 | 10.6% |
| APAC | 52 | 10.2% |
| India | 47 | 9.3% |
| Remote | 29 | 5.7% |
| United Kingdom | 26 | 5.1% |
| Unknown | 18 | 3.5% |
| Australia/NZ | 17 | 3.3% |

### Emerging hubs (last 30 days)

- **United States:** 227 recent vs 0 prior (+100%)
- **EU:** 54 recent vs 0 prior (+100%)
- **APAC:** 52 recent vs 0 prior (+100%)
- **India:** 47 recent vs 0 prior (+100%)
- **Remote:** 29 recent vs 0 prior (+100%)

## Disproportionate skills (Location Quotient vs industry peers)

LQ = (this company's share of postings with skill X) ÷ (peer industry's share of the same skill). Values >1 mean over-indexed; >2 means twice as common as peers.

| Skill | Postings | This company % | Peer industry % | LQ | Required % |
| --- | ---: | ---: | ---: | ---: | ---: |
| AWS | 60 | 11.8% | 0.3% | 40.45 | 87% |
| Azure | 60 | 11.8% | 0.3% | 40.45 | 88% |
| Google Cloud | 55 | 10.8% | 0.3% | 37.08 | 87% |
| SQL | 68 | 13.4% | 0.6% | 22.92 | 94% |
| Privacy | 14 | 2.8% | 0.1% | 18.88 | 100% |
| Kafka | 13 | 2.6% | 0.1% | 17.53 | 85% |
| CSS | 13 | 2.6% | 0.1% | 17.53 | 85% |
| Java | 87 | 17.1% | 1% | 16.76 | 93% |
| Cloud Computing | 24 | 4.7% | 0.3% | 16.18 | 63% |
| Cryptography | 12 | 2.4% | 0.1% | 16.18 | 100% |
| HTML | 11 | 2.2% | 0.1% | 14.83 | 100% |
| Cloud | 43 | 8.5% | 0.6% | 14.50 | 93% |
| Distributed Systems | 49 | 9.6% | 0.7% | 13.21 | 100% |
| CI/CD | 29 | 5.7% | 0.4% | 13.03 | 100% |
| Data | 9 | 1.8% | 0.1% | 12.14 | 89% |

## Salary positioning (USD, midpoint)

- **Company median:** $226,881 (68th percentile of industry)
- **Industry median:** $206,500 (sample n=20)
- **Disclosure:** 4% of company roles disclose salary vs 7% across industry

> ⚠️ Salary disclosed on fewer than 20% of roles — sample is small, treat ranges as indicative.

## Executive-level hires

- **Last 30 days:** 43 exec/director-level postings
- **Prior 90-day average:** 0 per 30-day window
- **Growth:** +100%

### Notable exec roles (recent)

- **Product Marketing Director, Lakewatch** — United States _(posted 2026-04-25)_
- **Director, Enterprise Sales** — Remote - Denmark _(posted 2026-04-24)_
- **Head of Corporate Engineering** — San Francisco, California _(posted 2026-04-18)_
- **Director, VC-Backed Startup Sales (West)** — Chicago, Illinois; Denver, Colorado; Los Angeles, California; San Francisco, California; Seattle, Washington _(posted 2026-04-18)_
- **Head of Marketing - Japan** — Tokyo, Japan _(posted 2026-04-18)_

## AI exposure

- **Occupation-weighted score:** 0.229 (0 = manual / low automation, 1 = high automation potential)
- **Skill-weighted score:** 0.44

### Most-exposed roles

- **Resident Solutions Architect - Communications, Media, Entertainment & Games** — 14 postings, score 0.199
- **Resident Solutions Architect - Public Sector** — 11 postings, score 0.199
- **Staff Software Engineer - Backend** — 10 postings, score 0.288
- **Resident Solutions Architect - Financial Services** — 9 postings, score 0.199
- **Solutions Architect** — 8 postings, score 0.199

## Peer set

| Company | Postings | Similarity | Momentum |
| --- | ---: | ---: | --- |
| [Synopsys](https://yubhub.co/companies/synopsys) | 677 | 43% | accelerating |

## Strategic themes

_Strategy briefs are generated weekly by an LLM analysis of the company's posting tag distribution. This section will populate once Phase 4 of the company-intelligence pipeline ships._

## Methodology & sources

- **Last enriched:** 2026-04-18T21:28:43.504Z
- **Company-data sources:** wikidata, github, news, hackernews
- **Job-data refresh:** continuous (queue pipeline). Aggregations cached at the edge for up to 24 hours.
- **Caveats:** salary parser uses simple FX (snapshot 2024 rates); long-tail location strings may bucket as "Unknown"; AI-exposure scores derive from O*NET 30.2 + Anthropic Economic Index 2026-03-24.

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Data: <https://api.yubhub.co/stats/company/databricks>