Job Description

Design, build, and maintain scalable ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and Apache Spark . Develop and optimize data lake and data warehouse architectures using AWS S3, Redshift, and Athena . Implement data ingestion, transformation, and storage solutions using AWS Glue, Kinesis, and Kafka . Work with structured and unstructured data to develop efficient data pipelines . Optimize query performance in SQL-based and NoSQL databases (Redshift, DynamoDB, Aurora, PostgreSQL, etc.). Ensure data security, compliance, and governance using IAM roles, KMS encryption, and AWS Lake Formation . Automate data workflows using Terraform, CloudFormation, or CDK . Monitor and troubleshoot data pipelines, ensuring high availability and performance. Collaborate with Data Scientists, Analysts, and Engineers to provide robust data solutions. Required Skills: Strong experience with AWS Data Services (Glue, Redshift, S3, Lambda, Kinesis, Athena). Proficiency in Python, SQL, and Scala Experience with ETL/ELT pipeline design and data warehousing concepts. Strong knowledge of relational and NoSQL databases (PostgreSQL, MySQL, DynamoDB). Familiarity with DevOps practices and CI/CD tools (Jenkins, GitHub Actions). Experience with Kafka or Kinesis for real-time data streaming. Knowledge of Terraform, CloudFormation, or CDK for infrastructure automation. Hands-on experience with data security and governance . Strong analytical and problem-solving skills