Tech Mahindra Data Engineer Interview Guide

Across the four candidate reports, the most consistent theme is that interviewers want you to explain work you have actually done. Candidates describe being asked to walk through current or past projects, and one was asked for the end-to-end architecture of a current pipeline, including Medallion layers, extraction frequency, the integration with GCP, and loading from Cloud Storage into BigQuery. The same candidate was asked what orchestration option they would use if Airflow were unavailable.

SQL and Spark appear repeatedly. One candidate reports scenario-based SQL and Informatica questions, such as combining values from three rows into one string, plus CTEs, duplicate removal, partitioning and clustering, RDDs and Spark job optimization. Another reports Spark architecture, Spark and Python coding, a window-function query, joins, database connectivity, data warehousing, data modeling and change data capture. A fourth describes a lighter first round on Python, SQL and Linux, then a harder round on Spark, big data, ETL and cloud platforms, with no live coding. That candidate was also asked about comfort with rotational shifts.

Process shapes differ. Reports describe two rounds with an HR round that did not happen, three rounds, and four rounds with two technical interviews, a manager round and HR. Manager conversations touched on project work, and in one case configuration, migration, data conversion and Oracle Cloud Applications. HR topics included preferred location, relocation, salary, benefits and career goals. Three of the four candidates did not receive an offer and one accepted. Prepare by rehearsing one real pipeline aloud and by practicing scenario SQL.