Principal Solution Architect

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/GCP ) 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 / GCP Architect , Azure/AWS Data Engineer , Databricks .