ApplySarthiBig Data Hadoop
Zensar
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This role on the market
2 open hadoop roles across 2 companies are on ApplySarthi right now, most of them in Pune (1).
What hadoop roles keep asking for: Spark (100%), Agile (50%), B2B (50%), BigQuery (50%), ETL (50%), GCP (50%), Hadoop (50%), SQL (50%) — counted across their open postings here.
Zensar has 265 open roles listed here.
- Automation Manager
- SERVICE DESK- 3 + Years - Punepune
- AES - DE - FS - Java Spring Microservices
- Master Data Management (MDM) Specialist
- GCP Data Engineering Bangalorebengaluru
Counted across 14 company job boards, updated as roles open and close.
Preparing for this interview
Interviews for hadoop roles keep coming back to Spark, Agile, B2B, BigQuery. Practise those questions before you sit with Zensar.
Questions you are likely to be asked
- Why do you want to join Zensar?
- What is your experience with Scala? Tell me one thing you learned the hard way.
- Tell me about a problem you solved at work that you are proud of.
- Tell me about a time you disagreed with your manager. What happened?
- Where do you want to be in three years?
Prep Sarthi gives you a free mock interview: an AI interviewer asks you questions like these out loud, from your own CV and this job, and shows your score and your weakest answer.
Practise the Big Data Hadoop at Zensar interview free →Scala, big data Spark Requirement Functional Programming: Proficiency with immutability, higher-order functions (map, flatMap, filter), and pattern matching is essential for writing efficient Spark code. Object-Oriented Programming (OOP): Familiarity with classes, traits, and abstract classes to organize complex data pipelines. Type Safety: Understanding Scala’s static typing helps catch errors at compile-time, which is critical for long-running big data jobs. Build Tools: Experience using sbt (Scala Build Tool) or Maven to manage project dependencies and package JAR files Scala, big data Spark Requirement Functional Programming: Proficiency with immutability, higher-order functions (map, flatMap, filter), and pattern matching is essential for writing efficient Spark code. Object-Oriented Programming (OOP): Familiarity with classes, traits, and abstract classes to organize complex data pipelines. Type Safety: Understanding Scala’s static typing helps catch errors at compile-time, which is critical for long-running big data jobs. Build Tools: Experience using sbt (Scala Build Tool) or Maven to manage project dependencies and package JAR files
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Listed on oraclehcm · posted 2026-09-23. ApplySarthi collects openings and links to application pages; the role is advertised by Zensar, not by us.