Coforge Data Engineer interviews reported by candidates run two to three rounds: a technical screen on SQL, PySpark or Spark, a managerial discussion, and sometimes a client or HR stage. Here is what each stage covered.
$90K
Avg. Base Comp
$108K
Avg. Total Comp
2-3 rounds
Typical Rounds
1 month
Process Length
Candidates describe the Coforge Data Engineer interview as practical rather than trick-based, and the first round carries most of the technical weight. One candidate reports a roughly one-hour screen in a plain notepad with no IDE: one SQL question, three PySpark problems and a dbt scenario, centered on window functions and dbt SCD Type 1 patterns. Another reports a first round on Spark and Scala fundamentals, DBMS theory, SQL query optimization and removing duplicates from a table, which led into indexing trade-offs and handling high-volume transactions. A third reports T-SQL, performance tuning and ETL scenario questions with a team lead.
The second round is described as managerial or techno-managerial. In these reports it centered on project experience, automation proof-of-concepts, problem-solving approach and client expectations rather than live coding.
Final stages differ. One candidate reports a client round with scenario questions on pipeline design with PySpark, Azure Data Factory and Databricks, including incremental loads and orchestration. Another reports an HR round that was mostly documentation.
All four reports in this set ended without an offer. One was a no-show by the interviewer, one cited sparse feedback, and in one an offer fell through after the client added DBA-style requirements. Expect some scheduling friction and keep your SQL tuning sharp. Prepare to explain your own projects clearly, since that was the focus of the managerial conversations.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Coforge process.
The process at Coforge for the Data Engineer role consisted of three rounds spread over several weeks. The first round was a technical screening covering SQL, PySpark, and dbt — I had about an hour to work through one SQL question, three PySpark coding problems, and a dbt scenario, all in a notepad environment with no IDE. The questions weren't overly difficult, focusing on practical concepts like window functions in both SQL and PySpark, and dbt SCD Type 1 patterns. The second round was managerial in nature, where I discussed experience, approach to problem-solving, and fit with the team rather than diving into code. The final round was with the client — typically conducted face-to-face if you made it that far — and they shifted to scenario-based questions. They asked about real-world data pipeline design using PySpark, Azure Data Factory, and Databricks, probing how I'd handle incremental loads, performance optimization, and orchestration decisions. The whole process took around a month. Overall the experience felt straightforward and fair; the technical bar was reasonable, and the questions were practical rather than trick-based. That said, the process seemed to move at a leisurely pace, suggesting the roles might not be high-priority staffing needs at the time.
Prep tip from this candidate
Focus on PySpark window functions and dbt SCD implementations in a bare-text editor environment — practice without autocomplete or syntax highlighting. Be ready for scenario-based client discussions on Azure Data Factory pipeline design, incremental loads, and Databricks Delta Lake optimization.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Coforge
Write a query to return duplicate rows
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report a first technical round covering SQL and PySpark or Spark. One describes about an hour in a notepad-style editor with one SQL question, three PySpark problems and a dbt scenario. Another reports duplicate removal, indexing trade-offs and ETL design. Practice without autocomplete.
Candidates report a second round that is more conversation than coding. Topics included past project experience, automation proof-of-concepts, problem-solving approach, and what the client would expect day to day. It may be run by a manager or lead, so prepare concrete stories about pipelines you built.
One candidate reports a final client round with scenario questions on pipeline design using PySpark, Azure Data Factory and Databricks, including incremental loads, performance optimization and orchestration choices. Other candidates did not report this stage, so it may depend on the team or client.
One candidate reports a third round with HR that was mostly signing client documents and sharing identification. That candidate's offer later fell through when the client added DBA-style SQL requirements. Candidates may want to confirm the job description is final before investing in later stages.
