Job description

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.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

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.