A top pharmaceutical company Data Engineer Interview Guide

Data Engineer candidates should prepare for a process that blends practical technical discussion with a clear account of their own work. One candidate reported beginning with an introduction and background conversation, then discussing project difficulties and how they responded. Another report similarly identifies résumé discussion and English communication for an international project. Have concise, concrete stories ready about a project obstacle, your decisions, and the result rather than relying on a high-level walkthrough of your experience.

Technical preparation should stay close to the reported tools and tasks. Candidates encountered SQL and ETL discussion, basic Python coding, and questions on Databricks, Azure Data Factory, and Terraform as infrastructure as code. A particularly important detail from one report is that coding transformations had to be written in PySpark; SQL was not accepted for that portion. Practice expressing routine transformations through the PySpark API, then be able to explain the logic behind them. That same candidate was asked to distinguish slowly changing dimension Type 2 from Type 3, making data-modeling fundamentals worth reviewing.

A campus-drive report followed a different path: an exam preceded an interview that combined basic Python with mostly behavioral questions such as introductions, strengths, weaknesses, and personal interests. The available reports are limited, so treat the tool mix and campus assessment as possible pathways rather than universal requirements. One report described three planned rounds—an introduction, technical interview, and director technical round—although that candidate exited after the first technical interview.