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Airflow Engineer

EXL • Business Process Outsourcing (BPO) • AI Consulting & Integration • Python • 2 months ago

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Location

In office (Chennai)

Seniority

Middle · 4+ years exp

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Aug 10, 2026. EXL scores B on the Alion truth index.

Overview

Company

Impact

Profile match

EXL

EXL, legally ExlService Holdings, is a data analytics, AI and digital operations company headquartered in New York that runs outsourced business processes and builds data and AI solutions for insurers, healthcare organisations, banks, media and retail companies. Founded in 1999 and listed on Nasdaq, it has more than 60,000 employees across six continents, with large delivery centers in Noida, Gurgaon, Pune, Bengaluru and Chennai and a newer AI hub in Dublin. It hires data scientists and data engineers, GenAI and MLOps engineers, analytics managers, solution consultants and client partners, plus talent acquisition, finance and medical coding staff.

exlservice.com • HQ: New York, United States • AI Consulting & Integration • Analytics & BI Consulting • Business Process Outsourcing (BPO) • 5000+ employees • Est. 1999

Tech stack of EXL

Analyze existing Cybermation job schedules, dependency patterns, runtime parameters, execution frequency, owners, success/failure behavior, and operational support expectations.
Review existing shell scripts and API/cURL invocation logic to determine the appropriate Airflow DAG design, task structure, error handling, and retry configuration.
Design, develop, and maintain Apache Airflow DAGs to orchestrate shell scripts, API calls, and dependent jobs with appropriate scheduling and operational controls.
Configure Airflow schedules, task dependencies, retries, logging, alerting hooks, variables, connections, parameters, and environment-specific configuration as per client standards.
Perform dry runs, unit testing, schedule validation, API response validation, failure/retry validation, and production-readiness checks for migrated jobs.
Create migration mapping, validation evidence, deployment notes, operational runbooks, and support handover documentation.
Coordinate with client SMEs, application owners, infrastructure teams, and release teams to support deployment, issue triage, initial run monitoring, and hypercare.

Analyze existing Cybermation job schedules, dependency patterns, runtime parameters, execution frequency, owners, success/failure behavior, and operational support expectations.
Review existing shell scripts and API/cURL invocation logic to determine the appropriate Airflow DAG design, task structure, error handling, and retry configuration.
Design, develop, and maintain Apache Airflow DAGs to orchestrate shell scripts, API calls, and dependent jobs with appropriate scheduling and operational controls.
Configure Airflow schedules, task dependencies, retries, logging, alerting hooks, variables, connections, parameters, and environment-specific configuration as per client standards.
Perform dry runs, unit testing, schedule validation, API response validation, failure/retry validation, and production-readiness checks for migrated jobs.
Create migration mapping, validation evidence, deployment notes, operational runbooks, and support handover documentation.
Coordinate with client SMEs, application owners, infrastructure teams, and release teams to support deployment, issue triage, initial run monitoring, and hypercare.

Bachelor’s degree or equivalent experience in Computer Science, Information Technology, Engineering, or a related discipline.
4+ years of overall engineering experience with hands-on Airflow, Python, and Linux/shell exposure.
Strong practical understanding of job scheduling, batch/workflow orchestration, production readiness, and operational support processes.

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