Description:

Zensar Technologies is hiring a specialized Data Engineer with deep technical expertise across the Amazon Web Services (AWS) cloud and distributed Big Data framework topologies for its Hyderabad location. In this role, you will design, engineer, and optimize complex data architectures to parse, wash, and store vast data streams. The accountability balances writing scalable production-grade pipeline components using PySpark DataFrames with performing rigorous query tuning to eliminate computational processing latency. Additionally, you will handle data warehousing model designs, configure cloud compute triggers, and provision decoupled system message queues to ensure high-speed enterprise data access.

Key Technologies:

Python, PySpark, Git, Amazon EMR, Amazon Athena, AWS Glue, Amazon Lambda, Amazon EC2, S3, SNS

Requirements:

• 4–5 years of professional software development experience explicitly focused on Big Data engineering technologies.
• Strong hands-on engineering proficiency using Python and PySpark. Proven ability to build and structure complex PySpark applications using Spark DataFrames.
• Comprehensive experience analyzing, tuning, and optimizing distributed Spark cluster execution plans to cost-effectively process massive, multi-terabyte datasets.
• Direct operational experience configuring managed analytics systems—specifically setting up Apache Spark clusters inside Amazon EMR, executing serverless queries with Amazon Athena, and building managed metadata catalogs via AWS Glue.
• Experience managing core AWS Compute networks using Amazon EC2 and serverless event-driven microservices via Amazon Lambda, backed by decoupled data layers using Amazon S3 storage buckets and notifications via Amazon SNS.
• Proficient working knowledge of source control architectures, release branching patterns, and code commit workflows using Git.
• Familiarity handling columnar database files like Apache Parquet, optimized with efficient cluster data compression tools including Snappy or Gzip codecs.
• Good structural knowledge of foundational dimensional modeling paradigms, including designing facts, dimensions, Star Schemas, and Snowflake Schema topologies.
• Baseline operational awareness or practical querying experience with at least one managed AWS database cluster (such as Amazon Aurora, RDS, Redshift, ElastiCache, or DynamoDB).