• Act as the technical lead for Data Operations, owning the operational readiness of modern data pipelines built on Airflow, Spark, and cloud data infrastructure • Lead incident triage and resolution for Tier 1 and Tier 2 pipeline issues (data delays, job failures, data quality alerts, SLA breaches) • Establish clear runbooks, escalation paths, and operational best practices for pipeline support • Partner with Data Engineering to influence pipeline design with operability, observability, and supportability in mind • Design and improve monitoring, alerting, and dashboards for data pipelines and workflows • Implement automation to reduce manual intervention (auto-retries, self-healing workflows, standardized alerts) • Drive root cause analysis (RCA) and post-incident reviews to prevent recurring issues • Continuously improve pipeline reliability, performance, and cost efficiency • Mentor and upskill team members transitioning from SQL Server–centric workflows to Airflow, Spark, ELK stack, and distributed data systems • Create learning paths, documentation, and hands-on guidance for modern data tooling • Lead by example with hands-on troubleshooting, debugging, and operational support • Help establish a culture of ownership, quality, and continuous improvement within Data Operations • Work closely with Data Engineering, Platform, and Product teams to align on priorities and operational expectations • Serve as a bridge between legacy data systems and the modern data platform during the transition period • Provide feedback on operational gaps, tooling needs, and process improvements