Across these candidate accounts, the most commonly reported technical topics in the Epam Systems Data Engineer process are SQL, Python, and Spark/PySpark, but the mix is not universal. Candidates describe live coding on SQL joins and window functions alongside medium-difficulty Python problems, with an expectation that you actually write code rather than talk through it. Spark questions, when present, go beyond syntax. Candidates report questions on:
- Spark architecture and how queries actually execute, including how the optimizer works
- dataset versus dataframe distinctions
- cluster and PySpark optimization
- Databricks topics such as Delta Live Tables
Not every loop hits all three core topics. One Senior Bigdata Engineer candidate was tested on SQL and Python but also faced Hadoop, GCP, Java, and even UI-testing questions. Another Data Engineer candidate's technical round centered on SQL, Python, scalable pipeline design with real-time processing, and Azure Data Factory scenarios, with no Spark questions reported. Cloud exposure varies too. Some candidates faced Azure Data Factory and Databricks questions, others AWS or GCP, and one was asked to compare Parquet, Avro, CSV, and Delta.
Structure also varies. Some candidates describe an HR screen followed by one or two technical rounds. Others add an online coding test, a managerial round, or a customer-facing conversation. One recurring theme stands out. One candidate who cleared two rigorous technical rounds was rejected in a 20-minute managerial round, and another heard nothing back after being told they had passed every round. A strong technical performance alone does not guarantee progression.
When preparing for this role, expect:
- applied SQL and Python coding
- Spark fundamentals where Spark is part of the loop
- scenario questions on designing pipelines for large datasets
Synthesized from 9 candidate reports by our editorial team.
