Sr. Data Warehouse BI Engineer

Role & responsibilities

Key Responsibilities

• Snowflake Data Warehouse & Migration:
• Lead the migration from legacy SQL databases to Snowflake.
• Design and implement multi-tenant data warehouse architecture (RBAC, data partitioning, resource management).
• Develop and optimize ETL/ELT pipelines using Snowflake and AWS services (Glue, Lambda).
• Analytics & Visualization (AWS QuickSight):
• Build, maintain, and scale AWS QuickSight dashboards for business and customer insights.
• Integrate QuickSight with AWS Cognito for secure, role-based, multi-tenant access.
• Cloud & Data Engineering:
• Implement automated data ingestion frameworks using AWS services (S3, Glue, Lambda, Step Functions).
• Optimize Snowflake performance and cost (clustering, partitioning, caching).
• Collaborate on CI/CD for data pipelines using GitHub Actions or AWS CodePipeline.
• Ensure security and compliance (IAM, encryption, audit logging).

Required Skills & Experience

• 8-12 years of experience in data engineering and analytics.
• Deep hands-on expertise with Snowflake Data Warehouse (migrations, multi-tenancy, performance tuning).
• Strong SQL development and data modeling skills (Kimball, Data Vault, Star/Snowflake schemas).
• Proven experience with AWS QuickSight (dashboards, row-level security, Cognito integration).
• Experience building cloud-native data pipelines with AWS Glue, Lambda, S3, and Step Functions.
• Proficiency with CI/CD and Infrastructure-as-Code (Terraform, AWS CDK, or CloudFormation).
• A strong security and compliance mindset (IAM, encryption, data governance).

Nice-to-Have Skills

• Experience integrating Snowflake with other BI tools (Power BI, Tableau, Looker).
• Familiarity with real-time analytics (Kafka, Kinesis).
• Exposure to Telecom, Wireless, or Networking data use cases (e.g., network telemetry).
• Hands-on experience with AI/ML-driven analytics.