Frontend interview guide
Databricks
Lakehouse data platform. React/TypeScript across notebooks, dashboards, SQL IDE, ML workflows. Dense technical UI for data engineers.
Frontend engineers interviewing at Databricks — notebook, workflow, SQL IDE, or Mosaic/ML UI teams.
Last reviewed 2026-10-04
What they emphasise
- Technical UI depth — notebook cells, SQL editors, workflow DAGs, dense dashboards.
- Collaborative editing — notebooks are multi-user; CRDTs/OT come up.
- DSA bar — the loop still carries a non-trivial algorithmic component.
- Scale — Databricks is one of the largest B2B frontends in data.
The loop, round by round
5 rounds. Durations and formats are typical, not guaranteed — confirm the loop shape with your recruiter in the screen.
Round 1
Recruiter screen
~30 minCallWhat it covers
- Resume, level, team orientation.
What they're looking for
- Preparation on the data-platform space — generic answers read poorly.
Round 2
Technical phone screen
60 minLive codingWhat it covers
- A DSA or JS problem, often with a performance twist.
What they're looking for
- Correctness with named complexity.
- Clean iteration.
Round 3
Onsite: coding (two rounds)
60 min eachLive codingWhat it covers
- One algorithmic round.
- One UI build — often in Databricks' domain (code editor, notebook cell, SQL result table).
What they're looking for
- Complexity reasoning and clean UI build.
Round 4
Onsite: system design
60 minShared docWhat it covers
- A frontend-shape prompt in Databricks' domain — a notebook UI, a workflow DAG viewer, a dense SQL results table.
What they're looking for
- Honest treatment of multi-user editing, large-data rendering, performance.
Round 5
Onsite: behavioural / team-fit
45 minConversationWhat it covers
- Collaboration, growth, ambiguity.
- Behavioural stories mapped to Databricks' values.
What they're looking for
- Specific stories with measurable outcomes.
What each level expects
The bar you're being measured against — plus the failure modes candidates most often trip on at that level.
SDE 2 / mid
~2-4 yearsShips features end to end with mentorship.
What they expect
- Clean React/TS, DSA fundamentals.
- Comfort with data-dense UI.
Common failure modes
- A system design that treats a notebook like a plain form.
Senior
~5-8 yearsOwns a technical area.
What they expect
- A system-design round treating collab + perf as co-equal.
- Past-work stories with specific numbers.
Common failure modes
- Textbook pattern recall without adaptation.
Staff
~8+ yearsMulti-team technical direction.
What they expect
- Peer-level discussion.
- Multi-team leverage.
Common failure modes
- Senior+ output rather than Staff leverage.
How to crack it
Day-of advice. The specific moves that separate offers from no-offers when the content is already in your head.
- 1If you're not from the data space, learn SQL to a working level and understand the notebook pattern (Jupyter / Databricks / Colab).
- 2Prep one data-dense UI build — a result table with virtualisation, a notebook cell with execution state.
- 3In the system design round, treat collab and perf as co-equal — Databricks notebooks are both.
- 4Pre-write stories with specific numbers.
- 5Use Databricks (community edition) before the loop.
How to master it (over months, not days)
The longer-horizon work. These are the habits that pay off at Senior+ bars where cramming visibly fails.
- Build a dense technical tool on your own time — a mini notebook, a SQL editor, a workflow visualiser.
- Learn one CRDT library and one virtualisation library at working levels.
- Read the Databricks engineering blog.
- Study Monaco (the code editor behind Databricks' SQL IDE).
- Make writing a habit.
Resources
On-site content for the technical prep, plus short-list of external links worth your time.