AWS Data Engineer

Requirements:

• Experience implementing, supporting data lakes, data warehouses and data applications on AWS for large enterprises
• Programming experience with Python, Shell scripting and SQL
• Solid experience of AWS services such as CloudFormation, S3, Athena , Glue, EMR/Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, SM etc.
• Solid experience implementing solutions on AWS based data lakes.
• Experience in AWS data lake/data warehouse/business analytics
• Experience in system analysis, design, development, and implementation of data ingestion pipeline in AWS
• Knowledge of ETL/ELT
• End-to-end data solutions (ingest, storage, integration, processing, access) on AWS
• Architect and implement CI/CD strategy for EDP
• Implement high velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred)
• Migrate data from traditional relational database systems, file systems, NAS shares to AWS relational databases such as Amazon RDS, Aurora, and Redshift
• Migrate data from APIs to AWS data lake (S3) and relational databases such as Amazon RDS, Aurora, and Redshift
• Implement POCs on any new technology or tools to be implemented on EDP and onboard for real use-case
• AWS Solutions Architect or AWS Developer Certification preferred
• Experience developing business applications using SQL databases.
• Should have good experience with AWS Services – S3, Athena, Glue, Lambda, Step Functions, SQS, Redshift.
• Knowledge of Snowflake is advantage

​Responsibilities:

• Designing, building, and maintaining efficient, reusable, and reliable architecture and code.
• Build reliable and robust Data ingestion pipelines (within AWS, onprem to AWS, etc)
• Independently perform hands on development and unit testing of the applications.
• Work in a team environment with product, production operation, QE/QA and cross functional teams to deliver a project throughout the whole software development cycle;