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Aws Devops + Snowflake Engineer

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Hybrid

Aws Devops + Snowflake Engineer

Fractal Analytics

Aws Devops + Snowflake Engineer

Skills

SQL QueriesDockerReactNodePythonTesting & QASnowflakeAIAWSGitHubLinuxTerraform

1 month ago

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Important information


Contract type:

Permanent

Salary:

₹2,500,000 – ₹3,500,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

18 September 2026

What they need


Role: Senior DevOps Engineer AWS, Snowflake & AI Platform

Core profile:

  • AWS

Terraform

Docker

GitHub Actions

Python scripting

React build and deployment

Snowflake deployment automation

Linux and networking

IAM and secrets management

CloudWatch monitoring

CI/CD and production support

The DevOps Engineer should create separate or reusable workflows for:

Frontend Pipeline

  • Install Node.js dependencies

Run linting and unit tests

Build the React application

Perform security and dependency scans

Publish artifacts

Deploy to S3

Invalidate CloudFront cache

Run smoke tests

Python Backend Pipeline

  • Install Python dependencies

Run formatting and lint checks

Execute unit and integration tests

Run security scans

Build the Docker image

Push the image to Amazon ECR

Deploy to ECS or EKS

Run health checks and rollback if required

Snowflake Pipeline

  • Validate SQL and migration scripts

Apply schemas, tables, views, procedures, and roles

Deploy development-to-production changes through approval gates

Track migration history

Prevent unauthorized production changes

Support rollback or recovery procedures

Infrastructure Pipeline

  • Run Terraform formatting and validation

Execute terraform plan

Scan infrastructure for security issues

Require approval for production changes

Execute terraform apply

Store Terraform state securely in Amazon S3 with locking

AI and GenAI DevOps Skills

For an AI-enabled program, the DevOps Engineer should additionally understand:

  • Amazon Bedrock API deployment and access control

Claude model integration and environment configuration

Prompt and configuration versioning

RAG infrastructure and vector-store deployment

Model and prompt evaluation pipelines

AI response logging and traceability

Token usage, latency, and cost monitoring

Guardrails and content-safety configuration

Data privacy and PII controls

Model endpoint availability and fallback patterns

LLMOps/MLOps integration with application CI/CD

Snowflake-Specific Expectations

The engineer should be comfortable with:

  • Snowflake account, database, schema, warehouse, and role administration

RBAC and least-privilege access

Snowflake network policies and private connectivity

Warehouse sizing, auto-suspend, and cost controls

SQL deployment automation

Data quality and pipeline monitoring

Secrets and key-pair authentication

Environment promotion across Dev, QA, UAT, and Production

Snowflake integration with AWS S3 and IAM roles

Secure data sharing and audit logging

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