Job Title: Airflow And Databricks SME
Job Location: Hyderabad
Experiences: 5+ Years
Job Type: Full-time
Shift Timings: Rotational Shifts (24/7)
Work Mode: 5 Days Work from Office
Job Summary:
We are looking for a 5 to 8 years of experienced Airflow & Databricks Scheduling SME to manage, support, optimize, and govern enterprise scheduling platforms and workflow orchestration processes. The candidate will be responsible for designing and maintaining Airflow workflows, scheduling Databricks jobs, monitoring critical business data pipelines, resolving production issues, and ensuring SLA compliance across the enterprise data ecosystem. The role requires strong expertise in Apache Airflow, Azure Databricks, workflow scheduling, incident management, problem management, and production support operations.
Scheduling & Workflow Management
Design, develop, and manage enterprise scheduling workflows using Apache Airflow and Databricks
Create, monitor, and maintain Airflow DAGs for batch and real-time data processing.
Schedule and orchestrate Databricks notebooks, jobs, and workflows.
Manage complex job dependencies and event-based scheduling.
Ensure successful execution of daily, weekly, and monthly business-critical jobs.
Monitor job completion, SLA adherence, and schedule compliance.
Validate upstream and downstream job dependencies.
Configure and manage Databricks Job Workflows.
Monitor cluster performance and job execution.
Troubleshoot failed Databricks jobs and Spark execution issues.
Coordinate with Data Engineering teams for workflow enhancements.
Optimize Databricks job execution time and resource consumption.
Configure and manage Databricks Job Workflows.
Schedule Databricks notebooks and ETL pipelines using Airflow.
Monitor cluster performance and job execution.
Troubleshoot failed Databricks jobs and Spark execution issues.
Coordinate with Data Engineering teams for workflow enhancements.
Optimize Databricks job execution time and resource consumption.
Key Responsibilities Summary
The Scheduling SME will:
Own Airflow and Databricks scheduling platforms.
Ensure successful execution of enterprise data workflows.
Monitor and support business-critical batch processing.
Handle incidents, outages, and escalations.
Lead Major Incident Management activities related to scheduling failures.
Perform RCA and Problem Management.
Drive automation and platform optimization.
Ensure SLA adherence and operational excellence.
Collaborate with Data Engineering, Cloud, Infrastructure, and Operations teams
Mandatory Skills
Apache Airflow, Azure Databricks
Workflow Scheduling & Orchestration
Python, Py Spark, SQL
Azure Cloud, Linux/Unix, Git
Monitoring Tools (Splunk, Grafana, Dynatrace)
Education & Certifications:
Bachelor's degree in Computer Science or related field
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