Company: Uber
Level: L4
Position: SDE 2 [ Data Engineer ]

Preliminary Round:

• https://leetcode.com/problems/move-zeroes/description/
• SQL questions on Manager & Employee tables
• Basic questions on Spark , Data skewness, Data partitioning etc..,

Round 1:

• SQL questions on fact_trip table ( trip details, driver_id,rider_id,start_time,end_time,city_id ) , driver_signup ( driver_id,signup_date etc.., )

  1. Find driver_ids who have not taken trip in first 7 days of their signup ( Lot of optimizations were discussed based on data volume )
  2. Find top3 cities each month baased on number of trips as criteria
  3. Find total_time spent of each driver each day ( start_time and end_time may span across 2 days → can extend to multiple days )

• Given a sorted array of n elements, possibly with duplicates, find the number of occurrences of the target element. ( https://leetcode.com/problems/find-first-and-last-position-of-element-in-sorted-array/description/ )

• Several questions on spark optimizations

Round 2 ( LLD ) :

• Build a system to generate Top 10 movies by category by time frame in streaming platforms like Netflix.
  Input Table users_viewership: userid,movieid,date,starttime,endtime

  Top 10 criteria: Number of views & Atleast 80 percent of run time should be watched for each view count.

  1. Design an Algo for finding view counts of movies by using users_vieweship table ( Merge Intervals logic )
  2. Design Data model for remaining source tables required and for warehousing tables ( Facts and dimensions )
  3. Design ETL strategy for generating this report daily
  4. Write complete set of sql queries by using tables and generate final table for reporting

Round 3 ( HLD ):

• Design a data pipeline Netflix source clickstream events. Build a dashboard with hourly frequency for each location what are top trending movies .Crietria - Number of views per movie as [ View criteria ( Full movie watch ) ]

Round 4 ( HM ):

• Generic Behavioural questions

Verdict: Rejected as my LLD round & HM rounds feedback were not strong hires.