L 1 Support Analyst

EXL; Pune District; On-site; INR 600,000 - 900,000; Full time

Job summary

EXL is seeking an L1 Support Analyst to monitor Hadoop-based pipelines and Jenkins jobs, triage incidents, and manage tickets in ServiceNow. The role operates within the HITL EXLdata.ai framework to ensure zero downtime and SLA adherence.

The candidate will handle 24x7 shift rotations, alert validation, and escalation to L2 when needed, while contributing to knowledge base enrichment and continuous improvement of auto-resolution rates.

Qualifications

• Knowledge of Hadoop ecosystem, Oozie, Hive, Spark, job monitoring and troubleshooting.
• Familiarity with Airflow DAG monitoring and retry procedures.
• Experience with MFT and Jenkins for transfer and pipeline monitoring.
• Hands-on with AWS services in a data engineering context.
• Experience logging tickets in ITSM platforms like ServiceNow.

Responsibilities

• Monitor data pipelines, batch jobs, and workflows; respond within SLA windows.
• Log, triage, and escalate incidents using runbooks and knowledge base.
• Escalate P1 incidents with full context and ensure smooth closure.
• Prepare shift handover notes and contribute to knowledge base.
• Collaborate with AI-assisted tools and validate automated resolutions.

Skills

Hadoop ecosystem

Airflow monitoring

MFT transfers

Jenkins pipelines

AWS cloud services

ServiceNow ITSM

Log analysis

Command line basics

Incident triage

AI-assisted tooling

Tools

Hadoop Oozie

Airflow DAGs

MFT tools

Jenkins

AWS (S3, EMR)

ServiceNow

Job description

Role Overview

The L1 Support Analyst is a first-line operations engineer responsible for 24X7 monitoring, triage, and incident management of Banking Client data platform. Working within EXL's EXLdata.ai Agentic AI framework (Human In The Loop HITL model), the analyst monitors Hadoop-based pipelines, Airflow workflows, MFT jobs, and Jenkins automation to detect, log, prioritise, and resolve or escalation issues ensuring service continuity with zero downtime and adherence to SLA targets.

Key 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 (less 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; escalates to L2 when AI and L1 tooling cannot resolve the issue.

Required Skills & Experience

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 24X7 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.