Key Responsibilities
1. CI/CD Pipeline Management
- Design, implement, and maintain end-to-end CI/CD pipelines for continuous integration, testing, and deployment of data and application workloads.
- Integrate and automate workflows using AWS CodePipeline, CodeBuild, CodeDeploy, and Jenkins, ensuring zero-downtime deployments.
2. Infrastructure Automation & Provisioning
- Build and manage Infrastructure as Code (IaC) using Terraform, CloudFormation, or Ansible to automate the provisioning of AWS services and data environments.
- Implement auto-scaling, backup, and disaster recovery strategies for high availability and resilience.
3. Cloud & Data Production Management
- Deploy and manage Data Production environments on AWS, ensuring system reliability, data consistency, and minimal downtime during releases.
- Collaborate with data engineering teams to operationalize and optimize ETL/ELT pipelines, Spark clusters, AWS Glue jobs, and Iceberg tables.
- Manage production-grade Data Lakes and Lakehouses using S3, EMR, Glue, Redshift, Athena, and Lake Formation.
- Monitor and troubleshoot production issues using CloudWatch, CloudTrail, Prometheus, or Grafana.
4. Containerization & Orchestration
- Containerize and deploy applications using Docker and Kubernetes (EKS) for scalable and consistent environment management.
- Manage Helm charts and Kubernetes manifests for multi-environment deployments (Dev, QA, UAT, Prod).
5. Collaboration & Continuous Improvement
- Work cross-functionally with developers, QA, and data engineers to improve deployment efficiency and enhance platform reliability.
- Evaluate new tools and techniques to strengthen automation, observability, and security within the data and DevOps ecosystem.
Required Skills & Qualifications
- Bachelors/Master’s degree in Computer Science, Information Technology, or a related discipline.
- 8–10 years of hands-on experience in DevOps engineering, with strong exposure to data infrastructure and production environments.
- 6–8 years of hands-on experience in DevOps engineering, with strong exposure to data infrastructure and production environments.
- Proven experience managing AWS-based Data Platforms and Data Lakes, including S3, Glue, EMR, Redshift, Lake Formation, and Athena.
- Strong understanding of Data Lake implementations, Medallion Architecture (Bronze–Silver–Gold), and data lineage/governance principles.
- Expertise in AWS services such as EC2, CloudFormation, CloudWatch, CloudTrail, CodePipeline, CodeBuild, and CodeDeploy.
- Strong proficiency in Infrastructure as Code (IaC) tools like Terraform and CloudFormation.
- Experience with Docker, Kubernetes (EKS), and container orchestration for scalable data workloads.
- Scripting experience in Python, Bash/Shell, or Ruby for automation, deployment, and monitoring.
- Deep understanding of CI/CD pipelines and Git-based workflows.
- Hands-on experience in Data Production deployment, release management, and root cause analysis in real-world environments.
- Familiarity with Apache Spark, AWS Glue, and Iceberg preferred.
- Strong analytical, troubleshooting, and communication skills with the ability to engage directly with clients to identify and mitigate risks in infrastructure or deployment.
What We Offer
- Opportunity to work on cutting-edge Data Intelligence Platform redefining enterprise analytics.
- Dynamic and collaborative work culture at NAM office.
- Competitive salary and performance-based growth opportunities.
- Exposure to the complete data ecosystem—from ingestion to analytics to AI-driven insights.
- A chance to directly influence the evolution of a high-impact enterprise data product.
