Databricks Forward Deployed Engineer Interview Experience
Databricks · Entry level
When I went through Databricks, it honestly felt like a Palantir-style FDE loop had been transplanted over. The decomp round was so vague that if I skipped stakeholder questions and KPIs and jumped into architecture, I would have been dead.
Interview process
I got an offer for Databricks FDE, and the loop felt very structured but also pretty different from a normal SWE interview. Mine was fully remote, and the stages were recruiter, a rapid-fire technical screen, a separate practical coding round, a decomp round, and then leadership. The coding itself was not absurd LeetCode, which I appreciated.
The most important round was decomp, where I had to treat the interviewer almost like a client and clarify stakeholder, scope, and KPI before touching architecture. The whole process felt like they were testing whether I could own an end-to-end enterprise solution fast.
Interview rounds · 5
- 1
Recruiter screen
BehavioralI started with a very standard recruiter screen. It was totally non-technical and mostly just logistics plus a basic why-Databricks check.
- Q1. Why do you want to work at Databricks?
Q2. Where are you based out of?
How they answeredI just kept this part straightforward. It was the usual logistics, where I was based, general fit, and why I wanted Databricks. Nothing tricky happened here and I treated it like a standard recruiter call before the real technical stuff started.
- 2
Phone screen
SQLConceptSystem DesignData Pipeline DesignMachine LearningThe next round was the Databricks technical screen, and this was very different from the coding round. It was rapid fire, with about five smaller questions meant to sample my technical foundation and breadth across the FDE surface area.
Q1. Write me a SQL query.
How they answeredI approached it like a speed round. The point was not to overcomplicate it, but to show I could think clearly under time pressure and still explain what I was doing. This round is really about proving breadth, so I would answer cleanly and move fast instead of getting lost in edge-case perfection.
Q2. Walk me through a small architecture problem or mini decomp.
How they answeredI would quickly frame scope, stakeholder, and KPI first, then sketch the high-level pieces. Since FDEs need technical breadth, I tried to show I could talk apps, data, and possibly ML without going too deep. The important thing was covering ground fast and showing where my spikes were.
Follow-up questions- What are you optimizing for?
- What part of the stack would you design first?
- 3
Technical round
CodingAfter that I had a separate coding round. This one was a practical coding interview in a notebook, more like one focused implementation problem than a bunch of trivia, and it felt more easy-to-medium than hardcore LeetCode.
Q1. Solve this practical coding problem in a notebook.
How they answeredAsk a couple clarifying questions, then talk while coding and keep the solution simple (use basic data structures e.g., dictionaries, strings, lists, and loops). They care a lot about thought process, so I made sure I was narrating.
Follow-up questions- Can you talk through your approach before you code?
- 4
Technical round
DecompositionThe decomp round was the most distinctive one. I saw it as a high-level system design mixed with product sense and client-facing consulting, and it was much more conversational than a normal SWE system design.
Q1. You have city traffic data. Build me a system to improve traffic in this city.
How they answeredDo not jump straight into architecture! I’d first clarify the actual problem, scope, stakeholder, end user, data access, and the KPI we care about. Then I’d restate the goal concretely and go application first, then data/ETL, then any ML or weighted algorithm if the use case called for it. I’d talk through user flows, APIs, storage, and basic data modeling, but keep it high level instead of going into caching or sharding. The whole thing feels like delivering business value, rather than merely designing infrastructure.
Follow-up questions- What clarifying questions would you ask first?
- Who is the stakeholder and who is the end user?
- What KPI defines success?
- How would you break the solution into high-level components?
- 5
Final / onsite round
BehavioralCustomer InteractionProject DiscussionThe last round was a leadership interview. I expected it to focus on whether I could handle the client-facing ownership side of the FDE job and move quickly with a lot of autonomy.
Q1. Tell me about a client-facing experience.
How they answeredI emphasized times where I built for real end users, showed demos regularly, took feedback, and iterated. For this role, that kind of signal matters a lot because the interviewer wants to know I can operate directly with stakeholders instead of hiding behind a ticket queue.
Follow-up questions- How did you handle feedback from end users?
Q2. Tell me about a time you owned something end to end.
How they answeredI framed ownership as owning the whole solution. In FDE work that means front end, back end, data, and possibly GenAI as one coherent workflow. That end-to-end breadth is a big part of what they’re screening for.
Follow-up questions- What parts of the stack did you personally drive?
Q3. Tell me about a time you had to move very fast.
How they answeredI talked about shipping under a really compressed timeline. The best example for me was taking something from basically zero code to production-ready in 1-2 months. That matched what the role wants, which is moving like a startup implementation arm for the client while still making solid technical decisions.
Follow-up questions- How did you balance speed with getting it right?
Tips from the candidate
I would not prep for this like standard SWE and call it a day. For coding, get good at practical easy-to-medium problems and practice talking while you code.
For decomp, you need a framework or you’ll waste the whole hour wandering around a vague prompt. I’d always start with clarifying questions, scope, stakeholder, and KPI, then move into the high-level architecture. Also be very ready to show your spikes, whether that’s apps, data, or GenAI, because breadth matters but they still want to see where you’re especially strong.
Company culture
When I went through it, the FDE org felt very new and the interview process was actively being revamped. I got the sense Databricks was borrowing a lot from the Palantir FDE motion. The interviewers were not looking for super academic system design depth. They cared more about whether I could connect business value to a buildable solution, stay client-facing, and move fast.
Scheduling was flexible, everything was remote, and the loop felt time-boxed enough that interviewers would guide if I started going too far in the wrong direction.