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237 lead data engineer interview questions shared by candidates

Find the maximum value in all possible subarrays of size K (sliding window maximum problem). Given an array of integers and a window size K, return the maximum values as the window slides through the array. This is a LeetCode hard-level algorithm question that's rarely relevant for data engineering roles, which typically focus on SQL optimization, ETL pipelines, and data architecture rather than complex algorithmic problems.
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AWS Lead Data Engineer

Interviewed at Ascendion

3.8★
Jul 18, 2025

Find the maximum value in all possible subarrays of size K (sliding window maximum problem). Given an array of integers and a window size K, return the maximum values as the window slides through the array. This is a LeetCode hard-level algorithm question that's rarely relevant for data engineering roles, which typically focus on SQL optimization, ETL pipelines, and data architecture rather than complex algorithmic problems.

Context Given a night with X ride requests and Y available drivers in a fictional city, you have to develop a batch processing application that would aggregate and expose data coming from the matching engine. Every Z seconds, the matching engine tries to match every pair of request and driver that are available in the city. Some are matched, some are not. The results of each matching tick are stored in a set of files. Given this data we want to be able to get a overview of the marketplace health and multiple applications could follow such as heat maps. The metrics that we want to use for heat mapping are driver match rate and request match rate. Exercice Develop a microservice that will: Aggregate matching data by fetching new matching data and exploiting this data in order to aggregate driver match rates and request match rates by geo-spatial units of your choice. Expose an endpoint which return the adjusted values of request and driver match rate, following this formula.. Bonus Plot the driver and request match rate for one night
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Senior/Lead Data Engineer

Interviewed at Heetch

4.2★
Dec 24, 2018

Context Given a night with X ride requests and Y available drivers in a fictional city, you have to develop a batch processing application that would aggregate and expose data coming from the matching engine. Every Z seconds, the matching engine tries to match every pair of request and driver that are available in the city. Some are matched, some are not. The results of each matching tick are stored in a set of files. Given this data we want to be able to get a overview of the marketplace health and multiple applications could follow such as heat maps. The metrics that we want to use for heat mapping are driver match rate and request match rate. Exercice Develop a microservice that will: Aggregate matching data by fetching new matching data and exploiting this data in order to aggregate driver match rates and request match rates by geo-spatial units of your choice. Expose an endpoint which return the adjusted values of request and driver match rate, following this formula.. Bonus Plot the driver and request match rate for one night

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