Support Analyst

Technical Skills knowledge of Hadoop ecosystem: Oozie Workflow Manager, Apache Hive, Apache Spark — job monitoring, resubmission, and basic troubleshooting. Familiarity with Apache Airflow: DAG monitoring, task failure identification, and manual trigger/rerun procedures. Experience with Managed File Transfer (MFT) tools — monitoring transfer jobs, identifying failures, and initiating reruns. Working knowledge of Jenkins — pipeline monitoring, build status tracking, and failure alerting. Understanding of AWS cloud services (S3, EMR, CloudWatch, EC2) in a data engineering context. Experience using ServiceNow or similar ITSM platforms for ticket logging, triage, and workflow management. Ability to read application/system logs and identify error patterns; comfortable with command-line interfaces. Operational & Soft Skills 2–4 years of experience in IT operations or data platform support roles. Ability to work in a rotational 24×7 shift environment including nights, weekends, and public holidays. Strong attention to detail; disciplined in following runbooks, SOPs, and escalation protocols. Clear and concise written communication for ticket documentation and handoff notes. Collaborative team player; comfortable working with AI-assisted tools and adapting as automation evolves. Ability to work calmly under pressure during high-severity (P1/P2) incident scenarios. Preferred Qualifications ITIL Foundation certification (v3 or v4). Exposure to data observability tools (Grafana, Prometheus, Cloud Watch or similar). Basic scripting skills (Python/Bash) for log parsing and ad hoc data checks. Experience in financial services or fintech data operations. Monitoring & Alerting Proactively monitor data pipelines, batch jobs, Oozie workflows, Airflow DAGs, MFT transfers, and Jenkins jobs across all platform components. Respond to automated alerts within SLA windows; validate, categorise, and action each alert using established runbooks. Monitor Hadoop cluster health (HDFS, YARN, Spark, Hive); report anomalies and trigger job restarts/reruns per standard operating procedures. Perform job monitoring and report cloud resource utilisation anomalies to the Lead. Incident Management & Triage Register and confirm all incoming tickets in ServiceNow; acknowledge the issue logger within SLA (<15 minutes). Categorise and prioritise tickets (P1–P4) using the Incident Triage Matrix; route to the correct resolution agent or support queue. Perform initial troubleshooting using runbooks, RAG-enabled knowledge base, and FAQ repository; resolve known/routine issues without L2 escalation. Escalate P1 incidents to L2 within 10 minutes with complete diagnostic context — logs, error messages, job details, and steps already taken. Manage incident workflow through to closure; update ticket status, document resolution steps, and close with accurate categorisation. Transition incidents to P2/P3 teams upon tiering changes; ensure smooth handover with full context preserved. Shift Handover & Documentation Prepare comprehensive Daily Handoff Notes at end of shift: open incidents, in-progress issues, critical observations, and pending escalations. Participate in shift-transition briefings with incoming analysts to ensure seamless knowledge transfer and continuity of monitoring. Contribute to knowledge base enrichment by documenting new issue patterns, resolutions, and lessons learned to support RAG-based knowledge updates. Agentic AI Collaboration (HITL) Operate as Human in the Loop (HITL) within the EXLdata.ai Agentic framework: review auto-triaged tickets, validate AI-suggested resolutions, and authorise automated remediation actions. Provide feedback to the Agentic AI resolution and feedback agents to improve knowledge base accuracy and auto-resolution rates over time. Utilise AI CoPilot suggestions to assist with complex ticket handling; escalate to L2 when AI and L1 tooling cannot resolve the issue. Graduate in Computer Science, B.Tech, B.E with 2+ years of hands-on data engineering experience.