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Control M Specialist Data Engineering

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Hybrid

Control M Specialist Data Engineering

CGI

Control M Specialist Data Engineering

Skills

SQL QueriesData EngineeringPythonDatabricksData AnalysisAWSLinuxChange Management

18 days ago

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Important information


Contract type:

Permanent

Salary:

₹1,500,000 – ₹2,500,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

7 October 2026

What they need


Control-M Specialist Data Engineering Portfolio – (Offshore)

Position Overview:
Experienced Control-M Specialist to support enterprise Data Engineering workloads, focusing on batch scheduling, workflow orchestration, data pipeline monitoring, dependency management, and production support.

Key Responsibilities:

  • Design, configure, schedule, and maintain Control-M workflows and batch jobs supporting Data Engineering and ETL/ELT processes.
  • Manage job dependencies, calendars, conditions, variables, alerts, and SLAs.
  • Schedule and monitor workloads across ADF, Databricks, AWS, data lakes, data warehouses, and other enterprise data platforms.
  • Monitor production pipelines and troubleshoot failed, delayed, or long-running jobs.
  • Manage upstream/downstream dependencies across data ingestion, transformation, validation, and analytics processes.
  • Support Control-M job onboarding, migration, deployment, and scheduling changes.
  • Develop/support automation using Shell scripting and Python, where required.
  • Perform RCA and coordinate with Data Engineering, Application, Cloud, and Production Support teams.
  • Support production releases, incident/problem/change management, and critical batch-processing SLAs.

Required Skills:

  • 5+ years of hands-on Control-M experience in an enterprise environment.
  • Strong expertise in Control-M scheduling, workflow orchestration, dependencies, calendars, monitoring, alerts, and SLA management.
  • Experience supporting Data Engineering, ETL/ELT, data warehouse, or data lake workloads.
  • Strong Linux/Unix, Shell scripting, and SQL skills.
  • Strong production troubleshooting and batch-processing experience.
  • Good understanding of enterprise data pipelines and upstream/downstream dependencies.
  • Strong communication and collaboration skills.

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