Role & responsibilities

What Youll Do

Operations Support

- Monitor and triage production data pipelines, ingestion jobs, and transformation workflows (e.g. dbt, Snowflake tasks)
- Manage and resolve data incidents and operational issues, working cross-functionally with platform, data, and analytics teams
- Develop and maintain internal tools/scripts for observability, diagnostics, and automation of data workflows
- Participate in on-call rotations to support platform uptime and SLAs

Data Platform Engineering Support
- Help manage infrastructure-as-code configurations (e.g., Terraform for Snowflake, AWS, Airflow)
- Support user onboarding, RBAC permissioning, and account provisioning across data platforms
- Assist with schema and pipeline changes, versioning, and documentation
- Assist with setting up monitoring on new pipelines in metaplane

Data & Analytics Engineering Support
- Diagnosing model failures and upstream data issues
- Collaborate with analytics teams to validate data freshness, quality, and lineage
- Coordinate and perform backfills, schema adjustments, and reprocessing when needed
- Manage operational aspects of source ingestion (e.g., REST APIs, batch jobs, database replication, kafka)

(confirm the writeup with Jason Prentice) ML-Ops & Data Science Infrastructure
- Collaborate with the data science team to operationalize and support ML pipelines, removing the burden of infrastructure ownership from the team




- Monitor ML batch and streaming jobs (e.g., model scoring, feature engineering, data preprocessing)
- Maintain and improve scheduling, resource management, and observability for ML workflows (e.g., using Airflow, SageMaker, or Kubernetes-based tools)
- Help manage model artifacts, metadata, and deployment environments to ensure reproducibility and traceability
- Support the transition of ad hoc or experimental pipelines into production-grade services.

Preferred candidate profile

- At least 24 years of experience in data engineering, DevOps, or data operations roles
- Solid understanding of up-to-date data stack components (Snowflake, dbt, Airflow, Fivetran, cloud storage)
- Proficiency with SQL and comfort debugging data transformations or analytic queries
- Basic scripting/programming skills (e.g., Python, Bash) for automation and tooling
- Familiarity with version control (Git) and CI/CD pipelines for data projects
- Strong troubleshooting and communication skills — you enjoy helping others and resolving issues
- Experience with infrastructure-as-code (Terraform, CloudFormation)
- Familiarity with observability tools such as datadog
- Exposure to data governance tools and concepts (e.g., data catalogs, lineage, access control)
- Understanding of ELT best practices and schema evolution in distributed data systems

📌 Data Operations Engineer (Chennai)
🏢 3shool Technology Consultants
📍 Chennai