Executive summary

Databricks' Agent investment has not yet surfaced in job titles—and its fastest-growing product signal is not the most visible one

Databricks' Agent signal sits in its product and hiring language, not yet in its org structure. The larger shift in the hiring mix comes from a different line — Forward Deployed Engineer — and from a far less visible product, Lakebase. These three core findings draw on independent evidence from job text, current employees, and product keywords.

Key findings:

• Current employees with AI/ML titles: 137 (for comparison, Snowflake has 82)
• 6-month increase in Lakebase hiring mentions: 0.4% → 4.6% (July, month 7 peak)
• Peak increase in Forward Deployed Engineer hiring volume: May (month 5) low of 1.8/month → 71.5/month in August (month 8)
• Databricks year-over-year employee growth: 40.6%, compared to Snowflake’s 20.7% over the same period

FDE signal

Most of Databricks' hiring growth is concentrated in a single role: Forward Deployed Engineer

In Databricks job descriptions, the share of Forward Deployed Engineer roles rose from a May (month 5) low of 0.3% to 13.4% in August (month 8). Absolute hiring volume climbed from 1.8 to 71.5 roles per month, an increase of about 40×. The series declined across months 2 through 4, then accelerated sharply from June (month 6).

Monthly share of hiring: Forward Deployed Engineer, AI/Agent, and Engagement Manager

• Forward Deployed Engineer: 71.5 roles/month
• AI/Agent roles fluctuated between 2.1% and 4.0% over 6 months
• Engagement Manager roles fell from 3.3% to under 0.2%

Current headcount

• FDE: 13 people
• Snowflake: 5 people

Agent signal

Agent has entered the job descriptions, not yet the org chart

Over the past six months, Agent-related roles nearly doubled as a share of Databricks hiring, rising from 1.1% to 2.1%. The increase includes Engineering, AI Research, and Product/GTM, broadening the hiring footprint.

Monthly share: Engineering (Agentic Applications)

Engineering share declined from 97% to 56%.

Monthly share: AI Research

• Appeared in month 5, peaked at 37%.

Monthly share: Product/GTM

• Dipped in month 5, expanded from month 7 to 26%.

Product signal

The product reshaping the hiring mix is the one getting far less attention

The share of Databricks job descriptions mentioning Lakebase increased about 10× in six months, from 0.4% to a July (month 7) peak of 4.6%.

Monthly trends

• Lakebase peaked at 4.6%, Genie closed at 2.9%, Agent Bricks fluctuated between 1.4% and 1.2%, and Database Function rose from 1.4% to 1.7%.

Talent flow and organization structure

Talent flow comparison: Databricks vs. Snowflake

Larger source companies sending talent to Databricks:

• Amazon/AWS: 915
• Google: 731
• Microsoft: 523

Larger source companies sending talent to Snowflake:

• Amazon/AWS: 579
• Google: 375
• Microsoft: 481

Smaller source companies sending talent:

• Databricks: MongoDB: 56, Confluent: 55
• Snowflake: MongoDB: 36, Confluent: 35

Current employee function mix

• Databricks: 7,163 current talent
• Snowflake: 6,377 current talent

Talent roster

Databricks Talent

AI

• A●● R●●, Software Engineer, Applied AI (San Francisco)
• B●● A●●, Machine Learning Engineer, GenAI (San Francisco)

Database

• Y●● L●●, Lakebase (Madison)
• R●● D●●, Member of Technical Staff, Lakebase (Amsterdam)

Snowflake Talent

AI

• P●● J●●, AI Data Cloud Architecture, Solutions (Mountain View)
• A●● J●●, AI Architect (Bengaluru)

Database

• J●● T●●, Software Engineer, Database Security (San Francisco)
• K●● K●●, Senior Software Engineer, Database Security (Redmond)

Questions this report answers

• What does the report cover?
• What is the population and methodology?
• How should this report be cited?