Salaries · Databricks
Databricks Forward Deployed Engineer / Field AI
Databricks field engineering blends lakehouse depth with GenAI delivery (including Mosaic AI). This hub covers directional compensation, role scope, and how the job differs from classic SA / SE seats.
Comp figures are directional public aggregates. Titles blur across Field Engineering, Forward Deployed, and Solutions — always read the JD for coding bar and travel.
Role overview
Databricks FDE-shaped roles help customers make the lakehouse and AI stack succeed in production: Unity Catalog realities, pipelines that survive dirty upstreams, and GenAI features that pass security and cost review — not slideware reference architectures alone.
Compare adjacent careers: FDE vs Solutions Engineer and FDE vs AI Engineer.
Compensation (directional)
| Signal | Cited figure |
|---|---|
| Reported TC range | $200K–$380K |
| Often-cited median cluster | ~$255K TC |
| Senior / specialist | Upper band with equity mix |
Level deep-dives: Mid, Senior.
Technical focus
- Spark / SQL / Delta lakehouse patterns under customer governance
- Identity, catalogs, and cross-cloud networking constraints
- Mosaic / GenAI delivery: RAG, evals, cost and latency budgets
- Migration and modernization programs with measurable adoption
Stack context: FDE tech stack and skills matrix.
Interview prep
Expect data systems fluency plus customer scoping. Start with the Databricks FDE interview guide, then practice enterprise system design and decomp cases.
FAQ
Is this a pre-sales role?
Some seats lean pre-sales; FDE-labeled roles usually imply more post-sale production ownership. Ask about KPI model (ARR assist vs delivery outcomes) and coding bar in the first recruiter call.
How does Databricks TC compare to Palantir?
Bands overlap mid-career; Palantir remains the cultural reference for decomp-style interviews, while Databricks discussions skew more lakehouse + GenAI product fluency.
Related hubs
Jump across salary, interview, and role-comparison pages for the same decision path.