Databricks is a pioneering company in the data and AI sector, renowned for developing the Databricks Lakehouse platform. As a Data Engineer at Databricks, you'll embrace a role critical to driving advancements in data processing, real-time stream processing, and ETL frameworks.

This position demands robust technical proficiency in big data tools such as Hadoop, Apache Spark, and Kafka, as well as SQL and Python. The role involves designing and optimizing data pipelines, collaborating with cross-functional teams, and translating business requirements into efficient data designs.

In this guide, Interview Query will walk you through Databricks' interview process, highlighting key topics like technical coding assessments, HR rounds, and typical interview questions. Let's get you prepared to join one of the fastest-growing sectors at Databricks!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Databricks as a Data Engineer. Whether you were contacted by a Databricks recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Databricks Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Databricks hiring manager might stay present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Databricks Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Databricks’ data systems, ETL pipelines, and SQL queries.

The technical interview can include questions on: - Pseudocode for binary search and other basic algorithms - DBMS basics - Spark - OOP principles

Apart from these, candidates should be ready to handle problems involving 1-2 hard questions and 1-2 medium questions, typically at the level of Leetcode. This round ensures your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals.

Depending on the seniority of the position, real-scenario problems and advanced topics may also come into the picture.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your visit to the Databricks office. Your technical prowess, including programming and data engineering capabilities, will be evaluated against the finalized candidates throughout these interviews.

This includes in-depth discussions around your proficiency with Python, SQL, building and optimizing big data pipelines, and experience with tools like Hadoop, Spark, Kafka, and Databricks platform.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview.

Quick Tips For Databricks Data Engineer Interviews

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Databricks interview include:

• Master the Basics and Beyond: Ensure you have a strong foundation in data engineering principles like ETL processes, real-time stream processing, and database management systems. Databricks interviews deeply dive into technical concepts, so broad and deep understanding will be required.
• Hands-on with Databricks Tools: Familiarize yourself with Databricks Lakehouse platform, Apache Spark, Delta Lake, and MLflow. Practical knowledge and hands-on experience with these tools will set you apart.
• Behavioral Strength: Databricks values a collaborative culture. Illustrate your teamwork, problem-solving skills, and communication ability through well-rounded answers to behavioral questions during screening and onsite interviews.

Typically, interviews at Databricks vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• Develop a function str_map to determine if a one-to-one correspondence exists between characters of two strings at the same positions. Given two strings, string1, and string2, write a function str_map to determine if there exists a one-to-one correspondence (bijection) between the characters of string1 and string2.

• Design three classes: text_editor, moving_text_editor, and smart_text_editor with specific functionalities. Create three classes with the following functionalities:

• text_editor: Methods to write, delete, and get notes.
• moving_text_editor: Extends text_editor with a special operation to move the cursor.

• smart_text_editor: Extends text_editor with a special operation to undo actions.

• Write a function sum_pair_indices to find indices of two integers in an array that add up to a target integer. Given an array and a target integer, write a function sum_pair_indices that returns the indices of two integers in the array that add up to the target integer. Ensure the solution runs in O(n) time.

• Write a query to show the number of users, transactions, and total order amount per month in 2020. Write a SQL query to show the number of users, number of transactions placed, and total order amount per month in the year 2020. Assume the data is from the transactions, products, and users tables.

• Write a SQL query to compute the cumulative sum of sales for each product, sorted by product_id and date. Given a sales table, write a SQL query to compute the cumulative sum of sales for each product, sorted by product_id and date. The cumulative sum includes the price of all purchases of the product on the given date and all previous dates.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• What is the probability that a user has exactly 0 impressions? You have an audience of size A and a limited amount of impressions B. Each impression goes to one user at random. Calculate the probability that a user receives exactly 0 impressions.

• What is the probability that every person has at least 1 impression? Given an audience of size A and a limited amount of impressions B, where each impression is randomly assigned to a user, compute the probability that every person receives at least 1 impression.

• How would you determine if a two-sided coin is biased? You are given a two-sided coin that could be fair or biased. Design a test to figure out if the coin is biased and describe the outcome that would indicate bias.

• What is the probability that a user views more than 10 ads a day? Users view 100 posts a day on a social media website, with each post having a 10% chance of being an ad. Calculate the probability that a user views more than 10 ads a day and approximate this value using the standard normal distribution's cdf.

• Which Facebook Ads payment option should you choose and why? You have two options for paying Facebook Ads for your e-commerce product growth:

• Pay within 90 days with a 6% fee on the principal.
• Pay within 45 days with a 3% fee on the principal. Determine which option is more advantageous and explain your reasoning.

• What metrics would you use to track the accuracy and validity of a spam classifier model? You are tasked with building a spam classifier for emails and have built a V1 of the model. What metrics would you use to track the model's accuracy and validity?

• How would you build a model to bid on a new unseen keyword? You are working on keyword bidding optimization with a dataset containing keywords and their bid prices. How would you build a model to bid on a new, unseen keyword?

• How would you build a fraud detection model with a text messaging service for transaction approval? You work at a bank that wants to detect fraud and implement a text messaging service to allow customers to approve or deny transactions flagged as fraudulent. How would you build this model?

• Why has the number of job applicants been decreasing despite stable job postings? You are analyzing a job board where job postings per day have remained stable, but the number of applicants has been decreasing. What could be the reasons for this trend?

• What considerations should be made when testing hundreds of hypotheses with many t-tests? You are conducting numerous t-tests to test various hypotheses. What factors should you consider to ensure the validity and reliability of your results?

• Which Facebook Ads payment option should you choose for e-commerce growth and why? You have two payment options for Facebook Ads: pay within 90 days with a 6% fee or pay within 45 days with a 3% fee. Which option is more advantageous for your e-commerce product growth, and why?

What is the average salary for a Data Engineer at Databricks?

Q: What is the interview process at Databricks like?
The interview process at Databricks typically consists of multiple phases. Initially, there is a challenging technical code signal test followed by HR rounds that include behavioral questions and resume discussions. Candidates can expect 3-4 rounds in total, often covering topics such as DBMS, Spark, OOPs, and algorithmic coding problems involving platforms like Interview Query for practice.

Q: What are common technical questions asked in Databricks Data Engineer interviews?
Common technical questions may include writing pseudocode for binary search, determining string sizes, and solving medium to hard level algorithmic problems. You should also be prepared for questions on database management systems (DBMS) and Apache Spark.

Q: What skills are required for a Data Engineer position at Databricks?
Key skills include expertise in Python and SQL, experience with building and optimizing big data data pipelines, data modeling, and advanced working knowledge of relational databases. Familiarity with tools such as Databricks, Apache Spark, and data warehousing is essential. Additionally, knowledge of AI/ML and business intelligence tools like Tableau and Looker is a plus.

Q: What is the company culture like at Databricks?
Databricks prides itself on fostering a diverse and inclusive culture. The company aims to empower data teams to solve complex problems and drives innovation through a collaborative environment. They value creativity, inclusivity, and continuous learning.

Q: What benefits does Databricks offer?
Databricks offers comprehensive health coverage, a 401(k) plan, equity awards, flexible time off, paid parental leave, family planning support, gym reimbursement, an annual personal development fund, and mental wellness resources. They are committed to providing a fair and inclusive workplace.

Conclusion

Embarking on a career as a Data Engineer at Databricks offers the chance to be part of a dynamic and influential team at the forefront of data and AI technology. The interview process may be rigorous, with a mix of technical and behavioral questions, but it provides an excellent opportunity to showcase your skills and expertise in data engineering, DBMS, and coding. Success in this role means not only shaping the future of data engineering at Databricks but also contributing to groundbreaking advancements across various industries.

If you want more insights about the company, check out our main Databricks Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Databricks’ interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Databricks data engineer interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!