Databricks specializes in data and AI and hosts a data intelligence platform that over 10,000 companies use to unify and democratize data, analytics, and AI. Databricks is headquartered in San Francisco, offers hybrid and remote roles, and calls its employees, “Bricksters.”
Interested in becoming a Brickster? Below, we break down the Databricks interview process and top questions you should expect to answer.
What is the Databricks interview process?
The interview process at Databricks is a fairly standard process. The loops change slightly depending on the role, but consist of three main parts. Databricks responds quickly, so expect to hear back within a couple days of each round.
The Databricks interview process typically takes about one month and involves:
- Recruiter phone screen,
- Technical screen,
- Final round of 4-5 loops.
Recruiter phone screen
During the recruiter phone call at Databricks, expect to discuss your background, resume, and basic qualifications. Prepare to answer, “Why Databricks?” and talk through a past project you’re proud of. Ahead of your interview, research the culture and technology of Databricks and come with some questions to show your interest.
Technical screen
Databricks’ interview process continues with a 70-minute live coding challenge on CodeSignal. The online assessment has four questions, ranging from medium to hard difficulty, often with two medium and two hard questions.
Prepare for the following topics:
- Data structures and algorithms
- String processing
- String manipulation
- Graph search
- SQL and Python
- Graphs
- Linked lists
- Queues
Final round
The final round of the interview process at Databricks is conducted either on-site or virtually and consists of 4–5 one-hour interviews. The loops vary depending on your role. Expect one behavioral loop, one interview with the hiring manager, and several technical loops on domain-specific knowledge.
Top Databricks Interview Questions
Behavioral
- Tell me about yourself.
- Tell me about the accomplishment you are most proud of.
- Describe the types of projects you've worked on in the past.
- Why are you interested in Databricks?
- Describe a time when you were innovative in solving a problem.
Coding
- Demo LabelBox for an Autonomous Delivery Client.
- Given an array of string commands as input, output a correct file path.
- Design an algorithm to get the load on the server in the past 5 minutes, given the time stamps of requests.
- Create a load tracking hashmap with a function that can return the average put/get calls per second made within the last 5 minutes.
System design
- Design a distributed file system.
- Design a system like YouTube.
- Design a data lake to handle large amounts of streaming and batch data.
- Design a machine learning pipeline that can handle large datasets and automate the model training and deployment process.
- Design a system to ensure data consistency and fault tolerance in a distributed environment.
Machine learning
- Explain training and testing data.
- How do you ensure a deployed model remains up to date?
- How can we tell when a model needs to be refreshed?
- What's the importance of feature scaling and normalization?
- What are the advantages and limitations of linear regression?
- What's the difference between classification and regression?
Data science
- Statistics
- Forecasting
- DS concepts
- Linear regression
- SQL
- Window functions
- Python
- ML concepts
- Spark queries
Product management
- How do you prioritize and structure roadmaps, deciding what to build and when?
- How do you gather health data for deployed microservices?
- Debug a metric that was off by x percentage.
- What is a favorite product of yours, and how would you improve it?
- What's the difference between how you handle projects vs. how you handle products?
Databricks Interview Loops Explained
Behavioral
A behavioral interview is part of every candidates’ final round at Databricks. This loop assesses your culture and team fit. It focuses on questions about your past experience, collaboration, and problem-solving approach. Expect strategic and high-level questions from a panel of senior team members. The STAR method is a great way to organize your answers with specific examples. Learn Databricks’ core values to tailor your responses to its culture, too.
We are customer obsessed,
We raise the bar,
We are truth seeking,
We operate from first principles,
We bias for action,
We put the company first.
Coding
The coding final round interview is similar to the technical screen, but is more in-depth, more role-specific and relevant to real-world problems at Databricks, and more interactive. Expect to code live, answering one or multiple complex problems relevant to Databricks' technical stack, such as distributed systems, big data, Spark, and algorithms. Prepare to discuss your solution with the interviewer, including communicating your thought process, trade-offs, edge cases, optimization, and follow-up questions.
System design
At Databricks, the system design loop is fairly standard—expect to design a scalable system in a live whiteboard interview. Focus on your design, but also prioritize communication, so the interviewers understand your approach and thought process. Prepare to design a system relevant to the context of Databricks (think distributed systems, scalability, data storage, data pipelines, and cloud architecture). Be ready to iterate your design based on live feedback and answer follow-up questions. Before your system design interview, get familiar with Databricks’ platform and technologies, such as Apache Spark and Delta Lake.
Machine learning
As a data and AI company, ML engineers are essential at Databricks. For your machine learning interview at Databricks, expect an ML case study and questions about ML concepts pertaining to the Databricks ecosystem. The case study is a real-world task; for example, you may receive a dataset and a problem, and then train an appropriate ML model to solve the problem. For the discussion part of the interview, study ML fundamentals and learn how Databricks uses tools like Apache Spark, Delta Lake, and MLflow, so you can answer practical ML questions about the Databricks workflow. To ace your ML loop at Databricks, focus on ML algorithms, scalability, model deployment, model monitoring, model evaluation, and big data.
Data science
Data scientists are key at Databricks. The data science interview at Databricks includes a data case study and a conversational assessment on working with large datasets and data concepts, such as statistics and A/B testing. For the case study, expect to receive a dataset and problem statement. During your analysis and case study presentation, clarify your decision-making process, talk through your problem-solving, and prepare for follow-up questions or restraints. Study up on Databricks’ platform, tech, and current ventures, so you’re ready for practical questions. And practice with relevant technologies, like Python, SQL, R, Pandas, and PySpark.
Product management
Databricks’ product management interview includes a take-home case study, case study presentation, a conversational interview, and a technical assessment. For the case study, demonstrate your customer-first mindset, data-driven decision making, vision, and thoroughly communicate your thought process. For the conversational interview, prepare examples using the STAR method about how you led initiatives, resolved conflicts, or aligned diverse teams in the past. The technical assessment ensures PMs understand technical concepts key to Databricks, including big data concepts (e.g., data pipelines, ETL, distributed systems, and machine learning workflows), cloud platforms, and Spark.
Databricks Interview Tips
Know Databricks inside and out.
To set yourself apart, learn as much about Databricks, its data platform, andcurrent ventures as possible before your interview. Databricks wants candidates who can hit the ground running and who show a real interest in the company. This preparation helps you answer practical questions, too.
Databricks is known for its rigorous technical interviews.
At Databricks, your technical interviews are most important. Expect deep-dives into domain knowledge and questions that assess your ability on tasks similar to what you'll do day-to-day in the role. To prepare, study more complex topics than you would for interviews at other companies.
Databricks values problem-solving, adaptability, and collaboration.
Databricks prioritizes candidates with strong technical domain knowledge and ability to solve complex real-world problems. But Databricks also wants candidates who adapt to change with a growth mindset. Lastly, Databricks wants strong collaborators and communicators, so demonstrate strong communication skills in all interview loops, and highlight successful relationship-building across teams in the past.
FAQs
How should I prepare for my interview at Databricks?
Practice coding and technical questions. Databricks conducts thorough and challenging engineering interviews, which involve medium-hard questions and in-depth questions about the company. Also, highlight your communication skills and growth mindset. And learn about Databricks—familiarize yourself with its culture, values, platform, and products. Don’t forget to come with questions to show your interest, too.
Does Databricks offer remote work?
Yes! While a lot of Databricks roles are hybrid—remote work balanced with weekly team days in the office—some roles are fully remote. Find current open roles on Databrick’s Open Positions page.
Does Databricks hire students or new grads?
Yes! Databricks hires new graduates and also offers 12–16-week internships, typically in its offices in San Francisco, Mountain View, Bellevue, Amsterdam, Berlin, Belgrade, or Bangalore. New grad roles usually open around August of each year. Databricks wants interns who are current undergrad or grad students in computer science programs, understand deep learning, and proficient in software engineering using PyTorch. Look more into internships and new grad opportunities on Databricks’ Internships and Early Careers page.
More Databricks Interview Prep Resources
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