I recently completed the interview process with Databricks and wanted to share my experience. While I ultimately decided not to move forward due to a leveling mismatch, it was genuinely one of the most professional, challenging, and well-structured interview loops I've been through. Here’s a breakdown for anyone preparing.

Round 1: The Technical Phone Screen (1 Hour - Elimination)

This was a single, in-depth DSA problem focused on practical application.

• Question (IP Firewall Implementation): Implement a firewall that takes a list of "ALLOW" or "DENY" rules with IP/CIDR blocks (e.g., "192.168.100.0/24") and determines if a given IP address should be permitted.
• My Take: This was a nice problem that required careful string parsing and bit manipulation to convert IP addresses and CIDR notations into a comparable format (like 32-bit integers and masks). The core of the solution was iterating through the rules and performing bitwise checks to see if an IP falls within a rule's range. The interviewer was satisfied with the solution, and I received positive feedback to proceed.

Round 2: Onsite DSA I (1 Hour)

The first onsite round presented a complex, custom data structure problem.

• Question: You are given a ref_string and a src_string. The src_string is represented as a "cover"—a list of index pairs pointing to substrings within the ref_string. The task was to implement a delete(cover, index) function that removes a character at a logical index from the src_string and returns a new, valid cover.
• Follow-up (Maximal Cover): The problem was extended with the concept of a "maximal" cover, where no two adjacent blocks in the cover could be merged to form a valid substring that still exists in the ref_string. The task was to ensure the delete function now returned a maximal cover.
• My Take: The initial problem was manageable, but I stumbled a bit on the follow-up. Maintaining the "maximal" property while splitting and potentially re-merging blocks after a deletion was tricky. I discussed the approach but wasn't fully confident in it. The recruiter later informed me that some minor flags were raised here, which was fair feedback.

Round 3: Onsite DSA II (1 Hour)

Knowing I had to perform well here, I was focused and ready.

• Question (Multi-modal Pathfinding): Given a 2D grid with transportation modes (Walk, Bike, Car, Train), costs, and times for each mode, find the fastest path from 'S' to 'D' using only one mode of transport. If times are tied, choose the cheapest path.
• Follow-up: How would your solution change if you could switch transportation modes at any point, but each switch incurred a fixed time penalty?
• My Take: The base problem was a classic graph traversal, perfect for running Dijkstra's algorithm (or BFS if costs are uniform) four times, once for each mode. The follow-up elevates the problem significantly, requiring a more complex state in the priority queue, like (time, cost, row, col, current_mode), to correctly explore paths and account for the switching penalty. I solved both optimally and felt the concerns from the previous round were now resolved.

Round 4: LLD & Concurrency Deep Dive (1 Hour)

This was an intense and highly practical round focused on low-level design and concurrency.

• The Challenge: Design and implement a thread-safe EventWriter class. Multiple threads would call appendEvent() to write small data buffers to a single file. The key constraints were: high throughput, low latency, and durability (the function must only return after the data is confirmed to be on disk).

• The Deep Dive & My Solution: This was a grilling session where a full C++ implementation was expected. My approach evolved as we discussed trade-offs:

  1. The Naive Solution (and why it's bad): A simple approach is a single mutex locking the entire appendEvent function, which calls write() followed by fsync() for every event. I explained this would have terrible throughput due to high lock contention and the massive overhead of calling fsync for every small write.
  2. Introducing the Producer-Consumer Pattern: To decouple event submission from disk I/O, I proposed a classic producer-consumer model. The application threads (producers) add events to a thread-safe in-memory buffer (e.g., a locked queue). A single, dedicated background thread (consumer) is responsible for writing to the file.
  3. Batching for Throughput: The consumer thread shouldn't write one event at a time. I implemented logic for it to drain the queue and group multiple events into a single, larger buffer. This batch is then written to disk in one write() call, followed by a single fsync(). This drastically improves throughput by amortizing the expensive system calls.
  4. Solving for Durability & Latency: This was the crux. The appendEvent call needs to block until its specific event is durable. To solve this, I used std::promise and std::future. When a producer thread submits an event, it also creates a promise and passes the corresponding future back to the caller (or waits on it internally). The consumer thread, after successfully calling fsync() on a batch, iterates through the events in that batch and fulfills their associated promises. This unblocks the waiting producer threads, guaranteeing durability before they return.

The discussion covered mutexes, condition variables, batching strategies, thread pools, and the critical role of fsync. The interviewer was satisfied with the robust and layered solution.

Round 5: The Hiring Manager & Collaboration Round (1 Hour)

This was one of the best HM conversations I've ever had. It was a masterclass in behavioral interviewing.

• The Flow: The HM was technically sharp and a genuinely engaged listener. The conversation started with a simple prompt: "Tell me about a recent project you found impactful." From there, every subsequent question was an organic follow-up that dug deeper into that single project's context:
  • Challenges: "What were the biggest technical and non-technical challenges you faced?"
  • Conflict Resolution: "Were there disagreements on the technical direction? How did you handle them?"
  • Mentorship: "What role did you play in mentoring others on the team during this project?"
  • Leadership: "Describe an instance where you had to lead without formal authority to get something done."

This format tests for consistency and depth far better than scattered, unrelated questions. The round concluded with an excellent, transparent overview of the team's work and vision.

The Result & My Decision

I was thrilled to receive an offer for a Level 4 Software Engineer position. The compensation was very strong and was competitive with an L5 offer I held from another company.

However, Databricks has a general policy of not increasing levels for lateral hires, and my goal was to secure an L5 role. Due to this leveling expectation mismatch, I respectfully decided not to move forward.

Despite my decision, I can't speak highly enough of the process. It was rigorous, fair, and a true test of a senior engineer's skills.