Data Engineering Interview at top product based company | First Round

Data Engineering Mock Interview
In top product-based companies like #meta #amazon #google #netflix etc, the first round of Data Engineering Interviews checks problem-solving skills.
It mostly consists of screen-sharing sessions, where candidates are expected to solve multiple SQL and DSA problems, particularly in #python. We have tried to replicate the same things by asking multiple good SQL and DSA problems to check the candidate's problem-solving skills.
If you're preparing for a Data Engineering interview, this is the perfect opportunity to enhance your skills and increase your chances of success.
The mock interview simulates a real-life scenario and provides valuable insights and guidance.
You'll get to see how professionals tackle technical questions and problem-solving challenges in a structured and efficient manner.
By watching this mock interview, you'll learn effective strategies to approach technical questions and problem-solving scenarios, gain familiarity with the data engineering interview process and format, enhance your communication skills and ability to articulate your thoughts clearly, identify areas of improvement, receive expert feedback on your performance, boost your confidence, and reduce nervousness for future interviews.
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🔅 To book a Mock interview - topmate.io/ankur_ranjan/15155
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🔅 Ankur(Organiser) 's LinkedIn profile - / thebigdatashow
🔅Ankur Ranjan(Interviewer) 's LinkedIn profile -
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🔅 Pragya Jaiswal(Interviewee) 's LinkedIn profile -
/ pragya-jaiswal-9661b3192
Chapters:
#sql #dataengineering #interview interview #interviewquestions #bigdata #mockinterview #aws #dsa

[@DE_Pranav]

great questions, thank you for this video

[@sandeepmodaliar6980]

The Python Program is an interesting one, assuming a value in list as Store ID and k as the distance or proximity within which another store with the same ID shouldn't exist. We can store a list as value with count,start_indx, end_ indx. If count is 2 we check the diff i.e., end_indx-start_indx

[@tejachillapalli8812]

dictionary = dict()
for index, value in enumerate(nums):
if value in dictionary:
if abs(dictionary[value] - index)

[@user-rh1hr5cc1r]

parquet store the file in hybrid format, not column based(80 % true)..

[@mohitbhandari1106]

I think the first sql can be done using group by as well instead of window function

[@VishalSharma-lz6ky]

Awesome mock interview
And the last question was very good
How it saves time if you are reading from disk

[@sarathkumar-tr3is]

1.SQL solution:
select name from (
select e.name,d.department_name, DATEDIFF(day,e.hire_date,p.promotion_date) as day_count,
rank() over(partition by d.department_name order by DATEDIFF(day,e.hire_date,p.promotion_date) desc) as Rank
from employee e
join promotions p
on e.employee_id =p.employee_id
join departments d
on e.department_id = d.department_id) A
where rank =1;

[@sarathkumar-tr3is]

def distinct_ind(l,k):
dict={}
for i in range(len(l)):
if l[i] in dict:
if abs(i - dict[l[i]])

[@sarathkumar-tr3is]

2.SQL solution:
select name from (
select
e.name, DATEDIFF(day,p.promotion_date,l.leave_start) as d_diff
from employee e
join promotions p
on e.employee_id = p.employee_id
join leaves l
on e.employee_id = l.employee_id) A
where d_diff = 1;

[@kiranmudradi3927]

I think d_diff should be >=1. lets say if an employee got promoted on some date which falls on Friday. From Monday he is taking leave. which has d_diff =2 in this case this recored wont be counted right. Just sharing my thoughts of some edge case.

[@sarathkumar-tr3is]

@@kiranmudradi3927 hey thanks for covering that

[@yashbhosle3582]

SELECT name, department_name, MAX(DATEDIFF(promotion_date, hire_date)) AS longest_time
FROM employee
JOIN department ON employee.dept_id = department.dept_id
JOIN promotion ON employee.employee_id = promotion.employee_id
GROUP BY department_name
ORDER BY longest_time DESC;