Coforge Ltd. is Hiring For Big Data Engineer & Big Data Lead.

Coforge Ltd. is looking for experienced Big Data Engineers and Big Data Leads with strong expertise in Spark, Scala, and AWS to join our growing Data Engineering team.

Experience: - 5 to 10 Years.

Mandatory Skills: - Apache Spark, Scala & AWS Services

Job Locations: - Hyderabad, Pune & Greater Noida

Interview Details: -

Interview Date: 26th September 2026

Mode: Microsoft Teams (Video Call)

Time: 10 AM to 4 PM

Interview Duration: 45 Minutes

Share Your CV: - [email removed]

WhatsApp: [phone removed]

Note: Immediate Joiners Preferred.

Overview:-

We are seeking highly skilled Big Data professionals with hands-on experience in Spark, Scala, and AWS cloud technologies. The ideal candidate will be responsible for designing, developing, and optimizing large-scale data processing solutions, building enterprise-grade ETL pipelines, and delivering scalable data platforms that support critical business initiatives.

Key Responsibilities:-

Data Engineering & Development

• Design, develop, and maintain large-scale distributed data processing applications using Spark and Scala.
• Build and enhance data ingestion, transformation, and ETL pipelines using AWS services.
• Develop highly scalable and reliable data solutions for structured and unstructured datasets.
• Create reusable frameworks and components for large-scale data processing.

Cloud Platform & AWS:-

• Implement cloud-native data solutions leveraging AWS services such as:
• EMR
• S3
• Glue
• Lambda
• Athena
• Redshift
• Kinesis
• IAM
• CloudWatch
• Ensure security, governance, scalability, and cost optimization of cloud-based data platforms.

Data Processing:-

• Build batch and real-time streaming applications using Spark frameworks.
• Process and analyze high-volume datasets efficiently.
• Optimize Spark jobs for performance tuning, memory management, and execution efficiency.

Data Quality & Reliability:-

• Implement monitoring, logging, and alerting mechanisms.
• Ensure data quality, consistency, and integrity across data platforms.
• Troubleshoot production issues and provide timely resolutions.

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