Databricks: Data Engineering at Scale
Got my Databricks offer in early 2024. They’re growing insanely fast (IPO coming), and their interview process reflects both startup hustle and enterprise rigor.
What You’re Getting Into
Databricks builds the platform for data engineering and ML. If you don’t know what Spark is, learn it. If you don’t care about data pipelines, wrong company. They hire people who geek out about distributed data processing.
The Interview Journey
Recruiter Screen (30 min): They ask about data experience. I mentioned building ETL pipelines and using PySpark for ML – that got me to the next round. If your background is frontend or mobile, this might not be the place.
Technical Screen (1 hour): One coding problem plus data-focused discussion. My problem was about processing large datasets efficiently. They care about: Can you think at scale?
Onsite (4-5 rounds):
- Coding (2 rounds): Algorithm problems with data context. One was about streaming data processing, another about optimizing queries. Medium/hard difficulty.
- System Design: Design a data platform component. I got “Design a job scheduler for distributed computations.” Think about fault tolerance, priority queues, resource allocation.
- Domain Knowledge: Deep dive into distributed systems, databases, or ML. They asked about Spark internals – partitioning, shuffling, caching strategies.
- Behavioral: Questions about working in fast-growing companies, handling ambiguity, and collaboration.
What They Care About
- Distributed Systems: Understanding of Spark, MapReduce, distributed computing
- Data Processing: ETL, data pipelines, batch vs streaming
- Scalability: Handling petabytes of data, query optimization
- Algorithms: Graph processing, sorting at scale, aggregations
- Cloud: AWS, Azure, GCP – they run on all three
How I Prepared
- Learned Spark Deeply: Read “Learning Spark” book. Understood RDDs, DataFrames, and internals. Built a project using Spark. This was crucial.
- Studied Distributed Systems: Read “Designing Data-Intensive Applications” by Martin Kleppmann. Best book for this interview.
- Did 120 LeetCode Problems: Focus on problems involving large datasets, streaming, and optimization.
- Used Databricks: They have a free community edition. I ran notebooks, played with Delta Lake. Showed genuine interest.
- Prepared System Design: Practiced designing: data warehouses, streaming platforms, job schedulers.
Common Failure Points
Not knowing Spark. One candidate said “I can learn it on the job.” Didn’t get an offer. Databricks expects you to hit the ground running.
Also: Treating data processing like web development. They’re different. If you optimize for sub-second response time instead of throughput, you’re thinking wrong.
The Growth Chaos
Databricks is scaling fast. That means opportunity but also: changing priorities, new processes, reorganizations. If you need stability, go to an established company. If you want impact and can handle chaos, this is great.
I’ve talked to friends there – some love the pace, others burned out. Know yourself.
Comp: Very competitive, especially pre-IPO. Base + significant equity. Total comp rivals or exceeds FAANG. The bet is on their eventual IPO making the stock valuable.
Last Updated: February 2026