Big Data & Hadoop MapR Developer

Tata Consultancy Services Limited; Indore District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tata Consultancy Services Limited is hiring a Big Data & Hadoop MapR Developer to design and build scalable data solutions. You will work with MapR / HPE Data Fabric, Spark, Hive, and other Hadoop ecosystem tools to deliver large-scale data processing pipelines.

The role requires 5–12 years of hands-on development experience in MapR and Hadoop, with strong Unix/Linux scripting and SDLC knowledge. Locations include Indore among others in India.

Qualifications

• Hands-on development with MapR / HPE Data Fabric and Hadoop ecosystem.
• Experience with MapR-FS / Data Fabric File Store and Spark.
• Strong SQL skills and experience with Spark SQL / DataFrames.
• Good knowledge of HDFS, YARN, and MapReduce.
• Unix/Linux scripting and debugging performance in Big Data workloads.

Responsibilities

• Design, develop, test, and maintain scalable Big Data apps in MapR/HPE Data Fabric.
• Develop distributed data-processing solutions using MapR, Hadoop, Spark, Hive.
• Build and support batch and large-scale data-processing pipelines.
• Optimize Spark apps, Spark SQL, Hive queries, and UDFs.
• Collaborate with cross-functional teams across SDLC and production support.

Skills

MapR / HPE Data Fabric

Big Data ecosystem

Spark

Hive / HiveQL

SQL

Spark SQL & DataFrames

HDFS / YARN / MapReduce

Unix/Linux scripting

SDLC

Batch & streaming pipelines

Tools

MapR / Data Fabric

Spark

Hadoop suite

Job description

Greetings from TCS!

We’re Hiring: Big Data & Hadoop MapR Developer

Role: Developer

Primary Skill: Digital : BigData and Hadoop Ecosystem - MapR

Experience: 5–12 Years

Locations: Mumbai | Chennai | Hyderabad | Bangalore | Indore | Ahmedabad

About the Role

We are looking for experienced Big Data & Hadoop MapR Developers with strong hands-on development experience in MapR / HPE Data Fabric and Hadoop ecosystem technologies.

The role involves designing, developing, optimizing, and supporting scalable Big Data solutions and large-scale distributed data-processing applications.

Mandatory Skills

• Strong hands-on development experience with MapR / HPE Data Fabric


• Strong experience with Big Data and Hadoop ecosystem technologies


• Hands-on experience with MapR-FS / Data Fabric File Store


• Strong development experience with Apache Spark


• Strong hands-on experience with Apache Hive / HiveQL


• Good working knowledge of SQL


• Experience with Spark SQL and DataFrames


• Good understanding of HDFS APIs, YARN, and MapReduce


• Strong Unix/Linux scripting experience


• Experience developing large-scale distributed data-processing applications and pipelines


• Strong hands-on experience in Spark and Hive debugging, troubleshooting, and performance tuning


• Good understanding of SDLC and software development practices

Key Responsibilities

• Design, develop, test, and maintain scalable Big Data applications in MapR / HPE Data Fabric environments.


• Develop distributed data-processing solutions using MapR, Hadoop, Spark, and Hive.


• Build and support reliable batch and large-scale data-processing pipelines.


• Develop and optimize Spark applications, Spark SQL, Hive queries, and UDFs.


• Work with high-volume structured and semi-structured datasets.


• Perform debugging, troubleshooting, and performance tuning of Spark, Hive, and Big Data workloads.


• Work with data ingestion, transformation, processing, storage, and workflow orchestration components.


• Identify application and query performance bottlenecks and implement optimization solutions.


• Participate across the SDLC, including requirements analysis, design, development, testing, deployment, and production support.


• Collaborate with cross-functional teams and take ownership of assigned development deliverables.

Good-to-Have Skills

Experience with any of the following would be an added advantage:

• MapR-DB / Data Fabric Database


• MapR Streams / Data Fabric Event Streams


• Apache Kafka


• Apache HBase


• Apache Sqoop


• Apache Flume


• Apache Oozie


• Autosys or similar enterprise scheduling tools


• Java and/or Python


• Spark Structured Streaming


• Cloud or hybrid Big Data platform exposure


• Agile development methodology


• Banking / BFSI domain exposure


• Production support experience in Big Data environments