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Summary
I successfully interviewed for an SDE 2 (Data Engineering) role at Indeed, navigating through multiple rounds including DSA, SQL, System Design, Data Modelling, and Data Quality. I received an offer for the position.
Full Experience
Special Appreciation to the panels and the recruiters as they were by far the best people I have faced in my interview process journey.
Phone Screening 1 (KARAT): 45 mins
A DSA question based on hashmap and strings. Basically you were given recipes and raw materials and you need to find the dishes that can be prepared. Leetcode medium level.
2 sql questions which required knowledge of partioning and dense ranking.
Phone Screening 2 (KARAT): 45 mins
2 DSA questions were asked in this round. One was based on BFS similar to rotten oranges and 2nd question was based on the course schedule 2 with a minor tweak.
2 sql questions which required knowledge of partioning and dense ranking.
Onsite Rounds
Resume Deep Dive: 1 hour
This was taken by a hiring manager in which questions related to my resume and in depth spark applications were asked. Questions were mostly related to my proud projects and how i implemented the project along with few improvements and deep analysis on the various databases and use cases.
High Level Design 1 hour
This round was taken by a principal architect and a staff engineer where we discussed the creation of pipeline if one source is a database table and other source is API. I discussed the lambda architecture where we can have slow and fast path. The interviewers were helpful and gave keen insights on my answers.
Data Modelling Round 1 hour
This round was basically based on the creation of schemas for the fact and the derived tables based on the business usecase. The usecase was related to movie streaming and review generation. I had to create the derieved tables and write the sql queries for them. This was by far the most difficult round. I was able to pass this round by an inch. This too was taken by a staff and a principal architect.
DSA ROUND 1 hour
This round was a complete DSA round but the catch was that this was in python only. Since I code in JAVA so I asked them about to use JAVA. They helped me with the python syntax. The expectation was to solve 3 questions on the binary search tree and pass the testcases provided. The round went well.
Data Quality 1 hour
This round was a creative one. I was provided a pdf which consisted of 10 tables with data. I had to go through them in 10mins and find what can be the possible bottlenecks and the error if we persist these data in the production database and datawarehouse. I was expected to find the errors and give possible solutions and write storing rules of data population. This was by far the best round for me.
The recruiter informed me in 2 days that my reviews were very good and I was selected for the position.
Offer Evaluation: https://leetcode.com/discuss/post/6763608/indeed-l2data-engineering-offer-evaluati-4lb6/
Interview Questions (7)
Given a list of recipes and available raw materials, determine which dishes can be prepared. This was a DSA question based on hashmap and strings, considered a LeetCode medium level problem.
A Data Structures & Algorithms question utilizing Breadth-First Search (BFS), conceptually similar to the LeetCode 'Rotten Oranges' problem.
A Data Structures & Algorithms question based on the LeetCode 'Course Schedule II' problem, with a minor modification.
Discussion revolved around my resume, in-depth Spark applications, proud projects, how I implemented them, potential improvements, and deep analysis of various databases and their use cases.
Design a high-level data pipeline where one source is a database table and the other is an API.
Design schemas for fact and derived tables for a business use case involving movie streaming and review generation. This included creating derived tables and writing corresponding SQL queries.
Given a PDF with 10 tables of data, identify possible bottlenecks and errors if the data were persisted in a production database and data warehouse. Provide solutions and define storing rules for data population.