Description

Key Skills: Redshift, Pyspark, AWS, Data Warehouse, AWS Lambda, SQL, Python, CICD, Databricks, Terraform, DevSecOps, Snowflake, IAC, AWS components eg Kinesis SQS SNS

Roles and Responsibilities:

• Design and develop scalable data pipelines on AWS for batch and near-real-time processing.
• Build ETL/ELT workflows using AWS Glue and orchestration with Apache Airflow and/or AWS Step Functions.
• Develop data transformations using Python and PySpark, and implement complex SQL logic for analytics workloads.
• Model data for analytics using dimensional modeling and support data warehousing patterns in Amazon Redshift.
• Implement AWS security and access controls using IAM and support reliable operations for data services.

Skills Required:

• 7-10 years of relevant experience in data engineering on AWS.
• AWS data engineering experience across core services (Glue, S3, Redshift, Athena, Lambda).
• ETL/ELT pipeline development with Python and PySpark for data processing.
• Strong SQL and database concepts, including dimensional modeling for data warehousing.
• Orchestration experience with Apache Airflow and/or AWS Step Functions, plus IAM security fundamentals.

Good to Have:

• AWS Certified Data Engineer or AWS Certified Solutions Architect.
• Terraform and Infrastructure as Code (IaC).
• Streaming technologies such as Apache Kafka or Amazon Kinesis.
• CI/CD and DevOps practices, plus exposure to Databricks or Snowflake.

Education: Bachelor's degree in Computer Science or related field; Master's degree preferred

Key Skills

Education

Any Graduate