Data Engineer applicants have rated the interview process at Grid Dynamics with 2.5 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 75% positive. To compare, the company-average is 55.5% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Engineer roles take an average of 11 days to get hired, when considering 4 user submitted interviews for this role. To compare, the hiring process at Grid Dynamics overall takes an average of 19 days.
Common stages of the interview process at Grid Dynamics as a Data Engineer according to 4 Glassdoor interviews include:
Phone interview: 30%
Skills test: 30%
Background check: 20%
Group panel interview: 10%
Personality test: 10%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. I interviewed at Grid Dynamics in Feb 2026
Interview
A few stages: 1) HR screening 2) Technical interview.
Interviewer looked tired and wasn't interested in interview at all.
Of course you consider any company when you need a job, but this company is a waste of time
El proceso de entrevista consistió de varias fases, que incluían una entrevista técnica con Grid Dynamics, y otra con el cliente final.
La entrevista se centro alrededor del Stack de Big Data habitual: Spark, Hadoop, Cloud...
Interview questions [1]
Question 1
Cual es tu experiencia con Spark -> Preguntas sobre arquitectura, eficiencia, debugging
Cual es tu experiencia con Cloud -> Servicios que más has usado,
I applied online. I interviewed at Grid Dynamics (Gdańsk)
Interview
It was multi steps interwiew.
First interview was standart with HR.
Then TI from Grid Dynamics itself.
Then Interwiew with Delivery manager of project took place.
Then Technical interview with Manager from client side.
Final interview with client was also Technical.
Interview questions [1]
Question 1
I was interviewed to scala related position, but no scala was accessed.
Most of the question was related to Spark/Hive.
About basics as types of file systems for spark.
About data skeweness.