Principal Snowflake Engineer

Role Overview

We are seeking a Lead / Principal Snowflake Engineer to architect and build scalable, enterprise-grade data platforms on Snowflake. This role will own the end-to-end data lifecycle, including ingestion, transformation, semantic layer implementation, and delivery of Front-end application.

You will act as a technical leader and architect, driving platform modernization, enforcing engineering standards, and ensuring performance, scalability, and cost efficiency.

Key Responsibilities

1. Data Platform Architecture & Modernization

• Design and build scalable Snowflake data platforms using best practices
• Assess legacy systems and define modernization and migration strategies
• Establish architectural standards, governance frameworks, and reusable patterns

2. Data Engineering & Integration

• Develop end-to-end ELT pipelines from APIs, databases, SaaS platforms, and event streams
• Build reliable connectors with robust error handling, retry logic, and data consistency
• Transform raw data into clean, normalized, consumption-ready datasets

3. Data Modeling & Semantic Layer

• Design dimensional data models (fact/dimension, star/snowflake schemas)
• Implement business-friendly semantic layers aligned with enterprise reporting needs
• Build aggregations, pre-computed metrics, and optimized data structures for analytics

4. Snowflake Engineering & Optimization

• Develop advanced SQL transformations and implement performance tuning strategies
• Manage warehouse sizing, workload optimization, and cost governance
• Implement RBAC, data security, versioning, and data sharing mechanisms

5. BI & Analytics Enablement

• Align Snowflake data models with Power BI (DirectQuery and Import models)
• Optimize datasets for performance, scalability, and reporting efficiency

6. Data Quality, Observability & AI Enablement

• Implement data validation, monitoring, and alerting frameworks
• Ensure high reliability and trust in downstream data consumption
• Leverage Snowflake Cortex, Agentic AI patterns, and AI tools to automate workflows and improve engineering productivity

7. Leadership & Stakeholder Engagement

• Provide technical leadership and mentor engineering teams
• Collaborate with stakeholders to define business and technical requirements
• Drive adoption of best practices in Snowflake and modern data engineering

Required Qualifications

• 10+ years of experience in data engineering, data architecture, or related roles
• Strong expertise in Snowflake (data modeling, performance tuning, governance, security)
• Proven experience building end-to-end data platforms from scratch
• Deep knowledge of semantic layer design and BI alignment
• Advanced SQL expertise (window functions, PIVOT, GROUPING SETS, etc.)
• Experience with multi-source data integration (RDBMS, APIs, SaaS, streaming)
• Strong cloud expertise (Azure/AWS) with Snowflake integration
• Proficiency in Python for data engineering and automation
• Familiarity with Agentic AI concepts and AI-driven tools to improve development efficiency and automation

Preferred Qualifications

• Experience with dbt (models, testing, lineage, documentation)
• Exposure to data observability tools (SODA.)
• Experience with SnapLogic, AWS S3, or equivalent services
• Experience with Snowflake Cortex / AI-based workflows
• Domain experience in Operation Data ( Cloud FinOps, AI Tool Ops, Managed Services Data, Agile Delivery Data will be Advantage.

Success Criteria

• Ability to design, architect, and deliver Snowflake platforms end-to-end
• Strong focus on performance, scalability, and cost optimization
• Expertise in data modeling and semantic layer implementation
• Demonstrated technical leadership and stakeholder management