Databricks
Large (~7,000)
Elastic
Large (~4,000)
Best For
Databricks
Best for: Data engineers who want strong comp and deep technical pedigree at scale
Elastic
Best for: Professionals interested in Elastic
Ratings
DatabricksElastic
4.1
4.0
Overall
3.9
4.1
Work-Life Balance
4.0
4.1
Culture & Values
4.3
4.0
Comp & Benefits
3.8
3.8
Senior Mgmt
3.9
3.8
Career Opps
👍 82% Recommend
👤 88% CEO Approval (Ali Ghodsi)
👍 80% Recommend
👤 84% CEO Approval (CEO)
Culture Values
Databricks only
⚙️ Engineering-Driven🌱 Learning & Growth🌈 Diverse & Inclusive🎯 Direct Product Impact
Elastic only
🌐 Remote-Friendly🔓 Open Source⚖️ Work-Life Balance📡 Async-First
Shared
💎 Strong Comp & Equity
Pros & Cons
Databricks
- Engineers who built Spark, Delta Lake, MLflow — deep technical pedigree
- Strong compensation and equity packages consistently above market
- Hypergrowth means org changes are constant — teams restructure frequently
- Some legacy Spark-era tech debt that can slow down new features
Elastic
- Fully distributed from day one — genuine remote-first culture across 40+ countries
- Open-source DNA means engineers contribute to community, not just internal products
- Cross-timezone coordination means occasional early/late meetings
- Recent leadership shifts have introduced more process and oversight
Bottom Line
Databricks
Choose Databricks if you want elite data infrastructure work with strong equity — but brace for constant org changes.
Elastic
Elastic — check Glassdoor for detailed reviews.
Open Roles
Databricks — 842 jobs
💼 Sales / GTM
353
💻 Engineering
284
📋 Product
39
📣 Marketing
34
📊 Data
22
👤 HR / People
12
🧠 ML / AI
12
⚙️ Operations
11
🎨 Design
6
💰 Finance
5
⚖️ Legal
5
Elastic — 198 jobs
💻 Engineering
87
💼 Sales / GTM
67
📣 Marketing
8
📋 Product
7
👤 HR / People
4
🎨 Design
3
🧠 ML / AI
3
📊 Data
2
Summary
Databricks scores 4.1/5.0 on Glassdoor with a 3.9 work-life balance rating. Elastic scores 4.0/5.0 overall with a 4.1 WLB. That’s a 0.1-point gap on overall satisfaction.
Both companies share these culture values: Strong Comp & Equity. Databricks also emphasizes Engineering-Driven, Learning & Growth, Diverse & Inclusive, Direct Product Impact. Elastic also emphasizes Remote-Friendly, Open Source, Work-Life Balance, Async-First.
On the hiring front, Databricks has 842 open roles and Elastic has 198 open roles.