What are the responsibilities and job description for the Lead Principal Snowflake Engineer position at Anblicks?
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
- 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
- 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
- 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
- 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
- BI & Analytics Enablement
- Align Snowflake data models with Power BI (DirectQuery and Import models)
- Optimize datasets for performance, scalability, and reporting efficiency
- 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
- 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