Databricks FDE Interview Guide

Databricks’ FDE loop is designed to look for two things above almost everything else: can you build production systems, and would a customer trust you to do it for them? 

Please note: AI FDE at Databricks is a different role from FDE, but the interview process is similar. Wherever AI FDE loops seem to differ, we’ve called it out.

TL;DR the Databricks FDE interview loop has 5 steps:

Step 1: Recruiter screen 

Step 2: Technical screen 

Step 3: Hiring manager conversation 

Step 4: Onsite

• 4.1 Technical deep dives (ML, GenAI, DS)
• 4.2 Coding
• 4.3 ML/MLOps system design
• 4.4 Behavioral (x1–2)

Step 5: The executive round (for senior FDEs)

Now, in early 2026, Databricks began converting Resident Solutions Architect (RSA) roles into FDE roles. You should be prepared for some admin issues. One recent candidate reported their RSA loop was converted to an FDE loop halfway through. Other candidates have reported getting ghosted after 8+ interviews.

Confidentiality note: Some candidates (seems to be more common for senior roles) report Databricks does backchannel reference checks. 

So, they’ll reach out to mutual connections (not just the references you’ve given) without asking you. This is not confirmed policy but it’s been confirmed by a Prepfully coach to us. 

If the team you’re interviewing for includes any former employees from your current employer, Databricks could ask those employees to ask about you at your current workplace and you should assume your job search might not stay private, so plan accordingly.

FDE job descriptions call the team a “highly specialized, customer-facing software engineering team” that works with Databricks’ most strategic customers.

Basically, your job is to sit with a customer’s team, and design and build the system they want. One Databricks FDE on Blind described the day-to-day as broad with higher level tooling, along with some “social skills as well.”

Since this is a newer role, the job requirements are a good source of what Databricks wants. The postings ask for a wide range of technical skillsets from Spark internals, CI/CD, MLOps, hands-on experience in at least two major cloud ecosystems, and more.

While the exact rounds you will go through during the onsite can vary a little, Databricks usually provides a detailed PDF describing what to expect. 

You can also ask the recruiting team early on if they have any pointers for the process and prep.

The first screen is a standard conversation on logistics and your background info. The recruiter will also share your profile so it can be team-matched, so be precise with how you describe your experience as that could change your interview loop. They’ll also share the document we mentioned earlier that details the entire process for you.

Databricks recruiters have asked FDE candidates these questions:

• Why Databricks? Why do you think you’ll make a good FDE? 
• Have you ever worked directly with a customer or external stakeholder to build something?
• Have you got any experience with the Databricks stack?

You should also flip the table and ask the recruiter questions. One Databricks candidate who coached through Prepfully said "I did talk to my recruiter and he sort of hinted that I'd be asked something like designing a ride-sharing app like Uber."

Just a reminder that the FDE role at Databricks is new so the compensation structure is not very transparent as of now, that’s why we suggest you keep the comp discussion broad, and not name any numbers on your own.

Databricks’ FDE screen is an hour-long coding test where you’ll be pairing up with one of their engineers.

For the technical screen, Databricks FDE candidates have reported these types of questions:

• Here’s the written specs, please make it. (follow-ups):
• Great, extend and make it work for [XYZ]
• Can you add [X] feature?
• Let’s write some tests now to make sure everything is working smoothly.
• Is this your first time working with Scala? That’s alright, I’m going to give you a small problem just to see how you do with it.
• I wrote this code, only parts of it work. Find out why it’s failing and fix it.
• Given this API, implement it. (Followed by: what happens on invalid input?)

Ted Tomlinson, a Sr. Director of Engineering at Databricks, wrote the company’s guide to its interviews, and it says: “we focus less on algorithm knowledge and more on design, code structure, debugging and learning new domains.” Keep that in mind for the technical screen and the onsite as well. Additionally, Tomlinson recommends:

• Practice solving problems completely i.e. full working code.
• Write tests without looking at the solution. 
• Be fast when setting up a test harness so you have a quick edit/compile/debug loop during the interview.
• Review CS fundamentals and common data structures; also study systems concepts like multithreading, concurrency, locks and transactions.
• Do mock interviews. You can book one with a Prepfully FDE coach.

We suggest you follow Tomlinson’s advice and practice writing tests and setting up harnesses both manually AND using LLMs, since Databricks interviewers will evaluate your ability to work with AI as well.

1. Some Databricks questions run in stages. So let’s say you build against a spec, then the interviewer will add more features. This is why the test-harness advice matters more. If you’re slow with editing and debugging, the delay adds up.
2. Interviewers will purposefully ask you to work in languages and frameworks you’re unfamiliar with. They want to know how you learn and deal with entirely new problems. If that happens, the expectation is for you to go through and read the docs first.
3. Databricks names a lack of passion for the role as one of the most common interview mistakes, and lists low product familiarity as a telltale sign of it. Plus, at Databricks, the delivery stack is the platform itself, so platform vocabulary might also come up. That’s why we highly recommend you build something small in the free Databricks edition to get familiar with the platform. 

This conversation lasts 30 to 45 minutes and will mostly focus on your projects, but your HM can also ask some theoretical stuff.

For AI FDE candidates: Some candidates report that their hiring manager round also tested LLM fundamentals, for example, how do transformer models work under the hood? 

The HM wants evidence you’ve owned delivery end-to-end and can talk about the people around a project as fluently as the system itself.

FDE work at Databricks requires being able to scope technical work and also feeding implementation lessons back to Databricks engineering. You’ll also be coordinating with project managers and customer teams. For the HM round, come prepared with stories that demonstrate that expertise.

Some questions reported by Databricks candidates for the HM rounds:

• Can you walk me through one of your projects and explain the tech stack to me? 
• Have you worked with any cloud providers before?
• Can you tell me about a project you owned from the first conversation to prod?
• What do you know about Databricks? How do you think customers use our platform? 

For the onsite loop you’ll likely have a couple of technical deep dives, a practical coding round, ML/MLOps system design, and one or two behavioral rounds. Some candidates might even have a 1-to-1 round with a Databricks executive.

Databricks has general FDE roles as well as FDE roles for manufacturing, public sector, financial services, healthcare, retail and media & entertainment. If yours is one of these, the interview loop will be similar, but you should expect to be given customer scenarios and data from that industry.

These rounds will focus on ML, GenAI, and DS experience. You might even have more than one round for each of these.

Overall, the process is simple. The questions will mostly be built around your resume. A Databricks engineer will ask you to walk them through your past projects, and they’ll keep asking ‘why?’. “Why this model? Why did you ignore X? Why did you go with this setup? What issues did you have in production? How did you fix them?Databricks wants people with practical, production-level experience, and expects you to discuss the scale and architecture of the systems you’ve deployed.

For AI FDE: this round reportedly focuses on RAG and AI architecture-decision questions. We recommend you make your prep specific to LLM work. 

This will be sort of a progressive architecture challenge. Interviewers will usually start with a basic premise and keep adding layers. A common scenario Databricks interviewers ask is: 

1. I’m giving you a dataset, can you build a basic medallion architecture (Bronze/Silver/Gold) for it?
2. Great, let’s now add an ML pipeline please.
3. Okay, so the customer changed their mind and now they want real-time streaming instead of batch processing. How do you adapt?
4. Now, the client wants to deploy this across multiple clouds with strict governance. How do we tighten the security?

Unlike the ML system design round (where interviewers will ask you to code part of the system), this round will stay conversational and you will have to draw components on a whiteboard. 

Databricks candidates have been asked these questions in their technical deep dives:

• How did you evaluate your AI system’s outputs? Were there any big failures? 
• Did you fine-tune this? When do you decide to fine-tune vs. improve the context & prompts?
• How would you debug a model if the offline metrics look fine but the behavior in prod doesn’t look excellent?
• After deployment, a model’s predictions are drifting. How do you find out why?

When going through your work, we recommend you explain the decisions (even what might seem like minor ones) you made and for major decisions, alternate choices you considered and ultimately rejected. Also talk about the assumptions you made initially, and if there were any constraints you had to deal with.

Databricks' coding rounds are quite practical and systems-focused. Other than that, the process is very variable. Databricks guidance plays down algorithm recall in favor of design and debugging.

Databricks tests many candidates on how well they use AI to write code. Some candidates receive guidelines that discourage manual code writing, and so it’s possible you won’t write a single line of code yourself.

You will likely be expected to use an LLM or the Databricks Assistant, but first you have to manually set up the workspace. Once the assistant outputs code, you have to talk through what it's doing, and vet the output. 

Then, when you have the basics working, expect a curveball. A common one Databricks throws at candidates is about an Out-Of-Memory (OOM) shuffle error. Your interviewer will say "Your executor nodes just crashed while joining. How do you fix it?" 

This candidate on Blind described their experience with AI coding for a solutions architect role at Databricks. The interviewer gave them a domain and asked them to generate synthetic data, join the datasets, calculate metrics, and finally build a visualization.

These are the questions you should expect in the Databricks FDE coding rounds:

• Here’s a simple API specification. Implement this class (the question is from SWE coding rounds at Databricks, but you could get similar ones). The follow-up is: What happens if someone calls this method with an invalid input?
• Build this feature please.
• Now extend it.
• Now add persistence.
• Now it should handle concurrent callers.
• Design the data structure for [X].

Concurrency often comes up in Databricks SWE interviews. That’s an adjacent role, but you should brush up on thread safety for FDE interviews as well because if you’re asked to design an API, your interviewer will likely also ask about concurrency.

For this round, you'll get an open problem like designing an end-to-end AI solution for an enterprise customer. 

Now, at most companies, ML system design rounds focus on depth rather than breadth. But, for Databricks, depth along with practical execution is super super important, and the interviewer will ask you to implement a piece of the architecture you’ve come up with. You might even be asked to write a prompt and optimize an LLM’s output.  

As we noted before, if your role is tied to a certain industry, the prompt will revolve around that industry. A Prepfully FDE coach who has been on the other side of interviews can help you refine your ML system design skills for your industry and for Databricks. 

Databricks candidates have been asked these ML system design questions:

• One of our enterprise customers wants an AI solution so employees can search and ask questions about their internal docs, what would you recommend?
• A customer wants to move their pipeline from batch scoring to real-time predictions. What changes will you make to the architecture?
• How would you make security watertight for a customer whose data sits across different clouds?
• How do you take a prototype that’s sitting in a notebook and productionize it?

Our general advice for this round: discuss your areas of expertise early on in the conversation, because the interviewers tend to focus on that.

Like most design rounds, you'll be given a virtual whiteboard (Excalidraw or similar). A common mistake some candidates we’ve coached make is they discuss everything verbally but don’t update the board with the same amount of detail which makes it hard for the interviewer to follow what you’re doing.

We also suggest candidates ask a ton of clarifying questions, and discuss the data sources and constraints before committing to the architecture. 

For senior candidates, the expectation is for you to connect the technical decisions with the business value. You’ll also be expected to think about alternative approaches on your own e.g. static graphs vs. supervisor-style vs. the newer skills-first approach.

At more junior levels, it's totally okay to ask the interviewer for a pointer or two when locking in on the architecture. 

Hot tip: knowledge graphs are a big hit at Databricks right now; their employees at recent conferences have shown cases where knowledge graphs cut down token usage and latency in LLM systems. 

One of Prepfully’s FDE coaches said this about the annual Databricks conference:

For the FDE role, the behavioral questions are focused on finding out how you’d function in someone else’s organization.

Expect follow-up questions as well. For instance, you say growth is your main criterion for choosing a job; you’ll be asked “what are you doing right now to grow?” 

For the Databricks behavioral rounds, expect questions like:

• Have you ever disagreed with a stakeholder about the technical approach? How did it go?
• A customer asks for something you think is a bad idea. What do you do?
• Can you tell me about a time you had to learn a new technology or language super quickly in order to deliver something?
• Tell me about a time you had to explain a technical decision to someone non-technical who didn’t like the decision you made.

For senior FDE and AI FDE candidates, the final hurdle might be a 30-to-45-minute chat with a VP or Director. 

The questions seem to be asked quickly and without pause. 

Some candidates have passed the 5-hour technical loop only to be rejected at the executive stage. For this Blind user, it happened twice; first for a Senior Solutions Engineer role, the second time for the AI FDE role.

Some Databricks candidates have mentioned that executives spoke to them about Databricks' growth goals and wanted to see if they aligned with the user’s personal career motivations. We recommend you have a good story about “why you want to be here, not just anywhere.”

Since FDEs will be working with Databricks’ clients, the executive rounds are really just Databricks' way of making sure you have the polish and strategic clarity to hold your own in front of senior stakeholders.

They’re also reading how you carry the conversation. Are you confident and personable under pressure? Can you communicate and land your point without rambling?

According to job posts, and self-reported Blind data, the median total comp is $308K, with the top 25% reaching $604K. Keep in mind Blind users can skew towards higher earners. 

Job posts for Senior FDE usually start with a $250K base.

We can also look at Databricks SWE for the upper ceiling. SWE L4 has ~$424K average annual TC. And L5 has ~$613K median. Although, company insiders report the FDE pay is “drastically lower at the upper levels” than SWE.