DataOps Engineer

Job Overview:

• Design, implement, and manage CI/CD pipelines using tools such as Jenkins, Git, and Terraform.
• Manage and maintain Kubernetes (K8s) clusters for scalable and resilient data infrastructure.
• Develop and maintain observability tools and dashboards (e.g., Prometheus, Grafana, ELK stack) for monitoring pipeline and platform health.
• Automate infrastructure provisioning and deployments using Infrastructure as Code (IaC) tools, preferably Terraform.
• Collaborate with data engineers to debug, optimize, and track performance of data pipelines (e.g., Airflow, Airbyte, etc.).
• Implement and monitor data quality, lineage, and orchestration workflows.
• Develop custom scripts and tools in Python to enhance pipeline reliability and automation.
• Work closely with data teams to manage and optimize Snowflake environments, focusing on performance tuning and cost efficiency.
• Ensure compliance with security, scalability, and operational best practices across the data platform.
• Act as a liaison between development and operations to maintain SLAs for data availability and reliability.

• 4-8 years of experience in DevOps / DataOps / Platform Engineering roles.
• Proficient in managing Kubernetes clusters and associated tooling (Helm, Kustomize, etc.).
• Hands-on experience with CI/CD pipelines, especially using Jenkins, GitOps, and automated testing frameworks.
• Strong scripting and automation skills in Python.
• Experience with workflow orchestration tools like Apache Airflow and data ingestion tools like Airbyte.
• Solid experience with Infrastructure as Code tools, preferably Terraform.
• Familiarity with observability and monitoring tools such as Prometheus, Grafana, Datadog, or New Relic.
• Working knowledge of data platforms, particularly Snowflake, including query performance tuning and monitoring.
• Strong debugging and problem-solving skills, especially in production data pipeline scenarios.
• Excellent communication skills and ability to collaborate across engineering, operations, and analytics teams.

• Experience with cloud platforms (AWS, and/or GCP) and cloud-native DevOps practices.
• Familiarity with data cataloging and lineage tools.
• Exposure to container security, policy management, and data governance tools.
• Background in data modeling, SQL optimization, or data warehousing concepts is a plus.