Principal Data Architect (Snowflake)
Based in the United StatesFull-TimePrincipal
SalaryCompetitive compensation with bonus opportunities
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Job Details
- Required Skills
- SalesforceSnowflakeAzureData modelingdbt
Requirements
- Extensive hands-on experience with Snowflake architecture and data platform design at enterprise scale.
- SnowPro Advanced: Architect certification strongly preferred.
- Proven expertise designing medallion and lakehouse architectures and managing complex schema evolution.
- Strong experience with Azure data platforms, including storage, networking, private connectivity, and Azure SQL.
- Deep understanding of data governance, including RBAC, tagging, classification, and Snowflake Horizon for PII and regulated data.
- Experience with modern data tooling such as Fivetran, CData, dbt, Snowpark, and Salesforce integrations.
- Ability to translate technical architecture decisions into financial and business impact (cost modeling, trade-offs, ROI).
- Strong communication skills with the ability to explain complex systems to technical and executive audiences.
- Exposure to or interest in Snowflake Cortex, LLM-on-warehouse, semantic modeling, RAG design, or Cortex Search is a plus.
- Strong interest in AI tools and methodologies, with a willingness to adopt and evangelize emerging technologies.
Responsibilities
- Design and evolve scalable Snowflake-based data architectures, including medallion and lakehouse patterns with robust schema evolution strategies.
- Define and enforce enterprise-grade data governance and security frameworks, including RBAC, tagging, classification, and dynamic data masking for sensitive data.
- Lead integration architecture across modern data ingestion tools such as Fivetran, CData, dbt, Snowpark, and Salesforce.
- Collaborate with Azure platform teams to design secure storage, networking, and private connectivity architectures, including Azure SQL integrations.
- Develop cost and consumption forecasting models and communicate architectural trade-offs to executive and business stakeholders.
- Establish standards for data modeling, pipeline design, data quality, lineage, and observability across the data ecosystem.
- Partner with engineering, analytics, security, and compliance teams to ensure alignment with regulatory and enterprise requirements.
- Mentor engineers and promote system-level thinking and architectural best practices across teams.
- Champion the use of AI tools and modern data technologies to improve productivity and innovation.
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