What to expect in a TCS Data Engineer interview: Spark, PySpark, Databricks, Azure Data Factory, SQL and project walkthroughs, based on five candidate reports with varied formats and outcomes.
$98K
Avg. Base Comp
$120K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Reports on the TCS Data Engineer interview vary by candidate, but the common thread is that interviewers want you to explain work you have actually done. One candidate described a single panel of three interviewers and accepted an offer. Another went through a screening call, two technical rounds, a client interview and an HR round over about a month and a half, and also accepted. A third described a prescreen, two technical rounds and a management round, and did not receive an offer.
Technical questions center on Spark and cloud data tooling. One candidate was asked how to investigate a 1 TB PySpark job that slowed from 20 minutes to two hours, and to explain the AWS services used in their ETL pipelines. Another was asked about incremental loads with Azure Data Factory and PySpark, plus Hive, Sqoop and Spark. A third faced Databricks questions on cluster types, Auto Loader, Delta Lake Time Travel, OPTIMIZE and ZORDER.
SQL came up as plenty of queries, including join questions worded so that it was easy to second-guess how unmatched rows are treated. The format is the biggest variable. One no-offer report began with a three-hour online assessment mixing aptitude, verbal, email writing and heap and array problems, while another centered on system design for collecting data and making it usable for analytics and machine learning. One candidate also noted sparse updates between stages and roughly another month before the offer letter arrived.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Tata Consultancy Services process.
The technical round for this Data Engineer role at TCS was split into two phases, and I'd say the key to it is being able to talk credibly about work you've actually done. It opened with some easy questions, then moved into scenario-based ones where I had to explain how I'd approach a data problem rather than produce a perfect textbook answer. The topics I remember most were ETL tools, how Python compares to other ETL tools, cloud deployment, and working with cloud servers.
The SQL portion took the most concentration. There were plenty of queries, and a few join questions were worded in a way that made them easy to second-guess, so I'd slow down and reason through how each join treats matching and unmatched rows before committing to an answer. On the Python side, I was asked about the data libraries I use day to day. I was also asked about my dashboard experience, so don't treat that as a throwaway line on your resume.
The interview also covered data modelling and a broader check on how confident I was in my skills. A large part of the conversation focused on my past projects: the Python scripts I had written, the ADF pipelines I'd built, and the PySpark work I'd done. I was asked how I had met deadlines on previous projects and how I would improve performance and optimize a slow pipeline.
I received an accepted offer. The biggest takeaway for me is that the interviewers wanted to hear me explain my own projects clearly, not just recite definitions.
Prep tip from this candidate
Be ready to walk through your past projects in detail, including the specific Python scripts, ADF pipelines, and PySpark jobs you built, plus how you met deadlines and optimized performance. Also drill SQL join questions with tricky row-matching cases, since they came up as a point of confusion.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Openings differed by candidate. One had a short screening call asking whether they were comfortable with Spark, Python and Azure, and another described a prescreen. A third reported a three-hour online assessment covering aptitude, logical reasoning, verbal questions, an email-writing exercise and two heap and array coding problems.
Candidates described one or two technical conversations covering PySpark optimization, Azure Data Factory, incremental loads, Hive, Sqoop, SQL joins and Python libraries. One was asked about Databricks features such as Auto Loader and Delta Lake Time Travel, and another about system design for data collection and analytics.
Several candidates said interviewers spent significant time on past projects, including ADF pipelines, Python scripts, PySpark jobs and AWS-based ETL. Questions probed architecture, the choices behind it, how deadlines were met and how a slow pipeline would be improved, so real detail mattered more than definitions.
One candidate described a management round after two technical rounds. Another reported a client interview followed by an HR round, with the offer letter arriving about a month later. A third completed everything in a single three-person panel, so the closing stages are not consistent across reports.
