What You'll do:

•  Drive initiatives to implement and enforce best practices for data streaming, processing, analytics and monitoring infrastructure.
• Deploy and manage services on Kubernetes-based platforms such as Amazon EKS and Google Kubernetes Engine (GKE).
• Provision and manage cloud infrastructure using Terraform, ensuring best practices in security, scalability, and cost-efficiency.
• Maintain and optimize CI/CD pipelines using Jenkins, ArgoCD, and GitHub Enterprise Actions to support automated deployments and testing.
• Work with cloud-native data services such as AWS Kinesis, AWS Glue, Google Dataflow, and Google Pub/Sub, BigQuery, BigTable
• Familiarity with  workflow orchestration services such as Apache Airflow and Google Cloud Composer.
• Develop and maintain automation scripts and tooling using Python to support DevOps processes.
• Monitor system performance, troubleshoot issues, and implement proactive solutions to enhance reliability and efficiency.
• Implement SRE practices to improve service reliability, scalability, and cost-effectiveness.
• Analyze and optimize cloud costs, identifying areas for improvement and implementing cost-saving strategies.
• Ensure compliance with security policies and best practices in cloud environments.
• Drive adoption of company standards and influence data teams to align with best DevOps and SRE practices.
• Collaborate with cross-functional teams to improve development workflows and infrastructure.

What you bring:

Required Qualifications:

• 7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
• Strong experience with AWS and GCP data services, including Kinesis, Glue, Pub/Sub, and Dataflow.
• Proficiency in deploying and managing workloads on Kubernetes (EKS/GKE) in production environments.
• Hands-on experience with Infrastructure-as-Code (IaC) using Terraform.
• Expertise in CI/CD pipeline management using Jenkins, ArgoCD, and GitHub Enterprise Actions.
• Programming skills in Python for automation and scripting.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or CloudWatch).
• Strong understanding of SRE principles, including performance monitoring, incident response, and reliability engineering.
• Experience with cost optimization strategies for cloud infrastructure.
• Self-motivated and driven, with a strong ability to influence and drive changes across multiple teams.
• Ability to work collaboratively in an agile environment and support multiple teams.

Preferred Qualifications:

• Experience with data lake architectures and big data processing frameworks (e.g., Apache Spark, Flink, Snowflake, BigQuery).
• Familiarity with event-driven architectures and message queues (e.g., Kafka, RabbitMQ).
• Experience with workflow orchestration tools such as Apache Airflow and Google Cloud Composer.
• Knowledge of service mesh technologies like Istio.
• Experience with GitOps workflows and Kubernetes-native tooling.