Control-M Developer with Data Engineering Experience

TMS LLC•Jersey City, NJ

12h•Remote

About The Position

We are seeking an experienced Control-M Developer with strong Data Engineering expertise to design, develop, and manage enterprise-scale batch scheduling and data pipeline workflows. The ideal candidate will have hands-on experience in Control-M automation, data integration, and end-to-end data pipeline orchestration across modern data platforms.

Requirements

5+ years of hands-on experience as a Control-M Developer / Scheduler
Strong experience in Data Engineering or ETL development
Proficiency in Unix/Linux shell scripting
Strong SQL skills for data validation and troubleshooting
Experience supporting enterprise batch processing environments
Strong analytical and troubleshooting skills
Ability to work in fast-paced, production-critical environments
Excellent communication and cross-team collaboration
Ownership mindset with attention to detail

Nice To Haves

Experience with Python, Spark, or Scala
Exposure to cloud-based data platforms (Snowflake, Databricks, BigQuery)
Knowledge of CI/CD tools (Git, Jenkins, Azure DevOps)
Experience with monitoring tools and SLA reporting
Control-M certification is a plus

Responsibilities

Design, develop, and maintain Control-M job flows for complex batch and data workflows
Create and manage job dependencies, calendars, conditions, and alerts
Monitor, troubleshoot, and optimize batch failures and performance issues
Implement job automation, reruns, recovery, and SLA management
Collaborate with application, data, and infrastructure teams to ensure seamless scheduling
Develop and support data pipelines using ETL/ELT tools and scripting
Integrate Control-M with data platforms such as: Data warehouses (Snowflake, Redshift, BigQuery, Teradata, Oracle, SQL Server) Big data ecosystems (Hadoop, Spark)
Orchestrate workflows involving file transfers (SFTP, FTP), APIs, and cloud storage
Support data ingestion, transformation, validation, and downstream consumption
Ensure data quality, reliability, and performance across pipelines
Schedule and monitor workloads in AWS / Azure / GCP environments
Integrate Control-M with cloud-native services (S3, ADLS, Lambda, Databricks, etc.)
Use shell scripting / Python for automation and data processing
Implement CI/CD best practices for job and pipeline deployments