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Target Data Engineer
Summary
I recently went through the interview process for a Data Engineer role at Target. The interview focused on SQL queries and Python programming, with an emphasis on data handling and project-related scenarios. The interview had two rounds, with the first round consisting of technical coding and project-related questions, and the second round focusing on Spark and Hadoop.
Full Experience
Round 1 was a technical coding and project-related round. I was given several SQL queries to solve, including counting active projects, calculating the average number of days to complete a project, finding employees not part of any active project, and identifying employees with the maximum salary within each project. I also had to write a Python function to find the longest common prefix among an array of strings. The SQL queries required a good understanding of joins, subqueries, and aggregate functions. The Python question tested my knowledge of string manipulation and loops.
Round 2 focused more on project-related topics and technologies specific to Target's internal Hadoop platform. The questions were about migration, real-time scenarios, data handling, and data transformations. This round was more about understanding how data flows within the system and how to handle various data processing challenges. It required a solid grasp of big data technologies and practical experience with data pipelines.
Interview Questions (5)
Write a SQL query to count the number of active projects. An active project is defined as one with a status of 'IN_PROGRESS'.
Write a SQL query to calculate the average number of days it takes to complete a project. Only consider projects with a status of 'COMPLETED'.
Write a SQL query to list all employee IDs who are not part of any active project.
Write a SQL query to list, for each project, the employees who have the maximum salary within that project, along with their maximum salary.
Write a Python function to find the longest common prefix among an array of strings.
Preparation Tips
For the interview, I focused on strengthening my SQL skills, particularly with joins, subqueries, and aggregate functions. I practiced writing efficient queries for common data analysis tasks. For the Python question, I reviewed string manipulation techniques and loop structures. Additionally, I studied big data technologies like Spark and Hadoop, as the second round involved migration and data processing scenarios. I made sure to understand how data flows through systems and how to handle real-time data transformations.