AWS Data Engineer (PySpark)

Good to have: AWS, Pyspark, Terraform + Public Sector

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using PySpark on AWS.

• Build and orchestrate ETL/ELT workflows using AWS Glue and AWS Step Functions.

• Develop serverless applications and automation using AWS Lambda.

• Write clean, efficient, and maintainable Python/PySpark code following engineering best practices.

• Provision and manage cloud infrastructure using Terraform (Infrastructure as Code).

• Implement and maintain CI/CD pipelines to automate code deployment, testing, and infrastructure changes.

• Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and cost efficiency.

• Collaborate with business stakeholders to deliver data solutions.

• Follow DevOps, security, and coding standards throughout the engagement.

Required Skills

• Strong hands-on experience with PySpark and Python for data engineering.

• Experience developing ETL pipelines using AWS Glue.

• Proficiency with AWS Step Functions for workflow orchestration.

• Experience building serverless solutions using AWS Lambda.

• Hands-on experience with Terraform for Infrastructure as Code (IaC).

• Experience implementing CI/CD pipelines using tools such as GitLab, GitHub Actions, Jenkins, or similar.

• Good understanding of AWS services, data lakes, IAM, S3, CloudWatch, and monitoring.