DataOps Engineer

Virtusa

5 - 10 years

3 - 7 Lacs

hyderabad

Posted:1 day ago| Platform:

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Skills Required

dataops s3 snowflake python github data services airflow ai data engineering root cause analysis cloud native incident response clinical data data quality lambda java automation devops terraform aws infrastructure as code aws services

Work Mode

Work from Office

Job Type

Full Time

Job Description

Medidata Solutions is seeking a Senior DataOps Engineer to help scale and operate our next-generation clinical data platform
You will work closely with Data Engineering, Data Services and AI/ML teams to automate deployments, enhance observability, and ensure reliable operations for critical pipelines and services powering CTMS, EDC, and eTMF integrations
What You will DoAutomate deployment and monitoring of Snowflake, Airflow, and S3/Iceberg pipelines across multiple environments
Build and maintain CI/CD workflows using GitHub Actions and infrastructure as code with Terraform
Deploy, operate, and scale Python and Java applications on AWS services, including Lambda, ECS, EKS, and Step Functions
Implement Data observability dashboards, alerts, and incident response workflows
Operationalize GenAI and Bedrock-integrated pipelines with strong governance and reliability
Drive root cause analysis, reduce MTTR, and mentor DataOps peers
What You BringPrimary5+ years in DataOps / DevOps / Platform Engineering supporting large-scale data platforms
Must have: Strong hands-on experience with GitHub Actions, Terraform, and EKS
Proven track record deploying and managing Python and Java applications on AWS (Lambda, ECS, EKS, Step Functions, CloudWatch, S3)
Expertise in Snowflake, Airflow, and cloud-native data pipeline operations
Strong scripting and automation skills
SecondaryBonus: Experience with Iceberg, data quality frameworks, or GenAI pipeline operations
Why MedidataPlay a key role in scaling one of Medidatas most critical data platforms
Work with a modern cloud-native, multi-tenant, GenAI-integrated stack
Real impact on how global clinical trial data is delivered at scale

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