Principal Snowflake Architect

Key Responsibilities

• Lead the end-to-end architecture of modern cloud-based data platforms across Snowflake and other ecosystem tools (Databricks, Synapse, BigQuery, Lakehouse).
• Assess legacy environments and define modernization strategies, migration roadmaps, and target-state reference architectures.
• Design scalable data engineering patterns, including ingestion, modeling, orchestration, and performance optimization across batch, streaming, and real-time.
• Define and implement enterprise data governance, including metadata, lineage, quality, cataloging, access controls, privacy, and compliance—both within and beyond Snowflake.
• Enable analytics, ML, and AI use cases, including ML pipelines, MLOps, feature stores, LLM integration, RAG patterns, vector search, and Snowflake Cortex.
• Establish BI and visualization standards, semantic modeling, KPI definitions, and governed self-service analytics.
• Drive architectural best practices, conduct design reviews, publish playbooks/standards, and provide technical leadership across engineering and analytics teams.
• Collaborate with business stakeholders to translate requirements into scalable data products and measurable technical solutions.
• Monitor and optimize platform reliability, performance, and cost, applying FinOps principles and continuous improvements.
• Mentor and guide engineers and analysts, promoting high-quality engineering practices and architectural thinking.

Required Qualifications

• • 12+ years in data architecture/engineering or similar, with 5+ years designing cloud-scale data platforms.
  • Hands-on Snowflake expertise (data modeling, performance tuning, governance features, resource monitors, Snowpark).
  • Deep experience with at least one major cloud (AWS/Azure/Google Cloud Platform) and integrating Snowflake or alternative platforms (Databricks, Synapse, BigQuery) within that ecosystem.
  • Proven track record leading end-to-end data lakehouse modernization and migration projects.
  • Strong SQL and Python; mastery of ELT/ETL frameworks (e.g., dbt, Airflow/ADF/Glue, Fivetran/Matillion,..).
  • Solid understanding of data security, governance, and compliance (cataloging, lineage, data quality, access control, privacy).
  • Experience enabling analytics/ML workflows and collaborating with DS/BI teams; familiarity with feature stores, ML pipelines, and MLOps.
  • Excellent communication, stakeholder management, and architecture documentation skills.

Preferred Qualifications

• • Practical AI solutioning: RAG, vector databases, prompt orchestration (Semantic Kernel/LangChain), guardrails, and LLM integration (including Snowflake Cortex).
  • Streaming architectures (Kafka/Kinesis/Event Hubs), API-led integration, and event-driven patterns.
  • Data governance platforms (Purview, Collibra, Alation) and data quality tooling (Great Expectations/Alation).
  • MLOps with MLflow, SageMaker, Azure ML, or Vertex AI; exposure to feature stores (Databricks Feature Store, Feast).
  • Visualization best practices and enterprise BI deployment with Power BI/Tableau/Looker (RLS/OLS, semantic layers).
  • Certifications (nice-to-have): SnowPro Advanced Architect, AWS SA Pro / Azure Solutions Architect / Google Cloud Platform Architect, Azure/AWS Data Engineer, Databricks.