Job Category: Data Management

Job Description:

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.
- Transparent 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.

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