AWS Data Engineer

Responsibilities:
Design, develop, and maintain scalable data pipelines and ETL/ELT workflows on AWS.
Build and optimise data lakes and data warehouses using AWS services.
Develop and maintain data ingestion frameworks for structured and unstructured data.
Implement Infrastructure as Code (IaC) using Terraform.
Deploy, manage, and monitor applications using Kubernetes (EKS preferred).
Collaborate with DevOps teams to automate CI/CD pipelines and cloud deployments.
Ensure high availability, security, and performance of cloud-based data platforms.
Optimise data processing jobs for scalability, reliability, and cost efficiency.
Troubleshoot production issues and implement proactive monitoring solutions.
Work closely with data scientists, analysts, and software engineering teams to deliver business solutions.
Requirements:
4-8 years of experience in Data Engineering.
Strong hands-on experience with AWS Cloud services such as: S3 EMR, Glue, Lambda, Redshift, Athena, EC2 IAM, CloudWatch.
Proficiency in Python and SQL.
Experience building ETL/ELT pipelines.
Hands-on experience with Terraform for infrastructure automation.
Strong knowledge of Kubernetes (EKS preferred) and containerization using Docker.
Experience with DevOps practices and CI/CD tools (Jenkins, GitHub Actions, GitLab CI, or similar).
Experience working with version control systems such as Git.
Good understanding of data modelling, data warehousing, and distributed data processing.
Strong debugging, troubleshooting, and performance optimisation skills.
Preferred Skills:
Experience with Apache Spark or PySpark.
Knowledge of Apache Airflow or other workflow orchestration tools.
Experience with Kafka or other streaming technologies.
Familiarity with monitoring and logging tools.
Understanding of security best practices in AWS environments.

Experience
4-6 yrs