Data Engineer,

Job highlights

• 5+ years IT experience with hands-on Python, PySpark, Spark Dataframes, and AWS analytics and compute services
• Build and optimize PySpark applications, manage version control with Git, and work with AWS services like EMR, Athena, Lambda, EC2, and S3

Role & responsibilities

5+ years of overall IT experience, which includes hands on experience in Big Data technologies.

• Mandatory - Hands on experience in Python and PySpark.

• Build pySpark applications using Spark Dataframes in Python.

• Worked on optimizing spark jobs that processes huge volumes of data.

• Hands on experience in version control tools like Git.

• Worked on Amazons Analytics services like Amazon EMR, Amazon Athena, AWS Glue.

• Worked on Amazon’s Compute services like Amazon Lambda, Amazon EC2 and Amazon’s Storage service like S3 and few other services like SNS.

• Good to have knowledge of datawarehousing concepts – dimensions, facts, schemas- snowflake, star etc.

• Have worked with columnar storage formats- Parquet,Avro,ORC etc. Well versed with compression techniques – Snappy, Gzip.

• Good to have knowledge of AWS databases (atleast one) Aurora, RDS, Redshift, ElastiCache, DynamoDB.

Bigdata, AWS, python, Pyspark, AWS services - IAM, lambda, EMR, glue

Preferred candidate profile