What you’ll be doing:  

• Monitor and manage day-to-day operations of data pipelines, ETL jobs, and cloud-native data platforms (e.g., AWS, Databricks, Redshift). 

• Own incident response and resolution, including root cause analysis and post-mortem reporting for data failures and performance issues. 

• Perform regular system health checks, capacity planning, and cost optimization across operational environments. 

• Maintain and enhance logging, alerting, and monitoring frameworks using tools like CloudWatch, Datadog, Prometheus, etc. 

• Collaborate with development teams to operationalize new data workflows, including CI/CD deployment, scheduling, and support documentation. 

• Ensure data quality by executing validation checks, recon processes, and business rule compliance. 

• Work with vendors (if applicable) and internal teams to support migrations, upgrades, and production releases. 

How You Will Succeed:  

Automation and Self-Service Focus 

• Identify repetitive operational tasks and implement automation using Python, Airflow, Jenkins, or similar tools. 

• Enable self-service capabilities and alerting for platform users and stakeholders. 

AI-Ready Operations Mindset 

• Explore and propose how AI can be used to detect anomalies, predict issues, and accelerate root cause analysis. 

• Collaborate with internal teams to experiment with LLMs, bots, or ML models for improving operational efficiency. 

• Stay informed on emerging AIOps tools and work toward integrating them gradually. 

Continuous Optimization 

• Monitor pipeline performance and costs, and implement changes that optimize compute, memory, and storage usage. 

• Recommend and trial AI/ML-based approaches for pipeline tuning, scheduling, or resource allocation.