Hadoop Administrator/Big data engineer

Advance Career Solutions; Pune District, Chennai District, Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Advance Career Solutions is seeking a Big Data Engineer to design and build largescale data pipelines using Spark, PySpark, Scala and Hive for enterprise analytics platforms. Located in India with multiple offices, the role focuses on scalable data processing and quality.

You will optimize Spark transformations, write Hive SQL, and collaborate with architects to improve data architecture. 5–8 years of hands-on big data experience is required, with strong fundamentals in the Hadoop ecosystem.

Qualifications

• 5–8 years of experience in big data engineering.
• Proficient with Spark, PySpark, Hive, and Hadoop ecosystem.
• Experience with data ingestion, transformation and validation.

Responsibilities

• Develop and maintain Spark PySpark Hive-based pipelines.
• Write optimized Hive SQL and Spark transformations.
• Support ingestion, transformation and validation processes.
• Handle high-volume data processing.
• Ensure performance, scalability and data quality.
• Collaborate with architects on design improvements.

Skills

Spark

PySpark

Scala

Hive SQL

Hadoop Cloudera

Python

Job description

Role & responsibilities

We are looking for a Big Data Engineer to design and build largescale data pipelines using Spark PySpark Scala and Hive for enterprise analytics platforms

Skills: Hadoop Data Engineer/Big Data Engineer

Location: Chennai Bangalore Pune Mumbai

Exp: 5 to 8 Years

Mandatory Skills : Big Data Hadoop Ecosystem, Python

Responsibilities

• Develop and maintain Spark PySpark Hivebased pipelines
• Write optimized Hive SQL and Spark transformations
• Support ingestion transformation and validation processes
• Handle highvolume data processing
• Ensure performance scalability and data quality
• Collaborate with architects on design improvements

Skills Required

• Spark PySpark Scala
• Hive SQL
• Hadoop Cloudera
• Python exposure is a plus
• Strong data engineering fundamentals