Manpower Hiring! Full Time Data Engineer (AWS, PySpark) in - Ricebowl

Key Responsibilities

• Design, develop, and maintain scalable and high-performance data pipelines using Python and PySpark.
• Build, deploy, and manage ETL/ELT workflows using AWS Glue and AWS Step Functions.
• Develop serverless applications and automation solutions using AWS Lambda.
• Write clean, efficient, maintainable, and reusable code following software engineering best practices.
• Provision and manage cloud infrastructure using Terraform and Infrastructure as Code (IaC) principles.
• Design, implement, and maintain CI/CD pipelines to automate code deployment, testing, and infrastructure changes.
• Monitor, troubleshoot, and optimize data pipelines to ensure performance, reliability, and cost efficiency.
• Implement logging, monitoring, and alerting solutions using AWS CloudWatch and other observability tools.
• Collaborate with business stakeholders, solution architects, and technology teams to understand requirements and deliver effective data solutions.
• Support production deployments, incident resolution, root cause analysis, and continuous improvement initiatives.
Ensure all solutions adhere to security, compliance, DevOps, and coding standards.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related discipline.
• Minimum 5 years of experience in Data Engineering, with at least 3 years of hands-on experience working on AWS cloud platforms.
• Hands-on experience with Python and PySpark for large-scale data processing and transformation.
• Proven experience developing and managing ETL/ELT pipelines using AWS Glue.
• Proficiency in workflow orchestration using AWS Step Functions.
• Experience building serverless applications and integrations using AWS Lambda.
• Hands-on experience with Terraform for Infrastructure as Code (IaC).
• Experience implementing and managing CI/CD pipelines using tools such as GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps, or similar platforms.
• Good understanding of AWS services, including S3, IAM, CloudWatch, and data lake architectures.
• Experience in monitoring, troubleshooting, and optimizing cloud-based data solutions.
• Experience working with large-scale data platforms and distributed data processing environments.
• Excellent communication and stakeholder management skills, with the ability to work effectively across technical and business teams.
• AWS certifications such as AWS Certified Data Engineer, AWS Certified Solutions Architect, or AWS Certified Developer are good to have.