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.

Responsibilities

• 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.

Qualifications

• Graduate in Computer Science, B.Tech, B.E with 2+ years of hands-on data engineering experience.