Snowflake Lead Data Engineer

MUST BE LOCAL TO TX

Responsibilities

• Lead the end-to-end architecture and implementation of Snowflake(Bronze/Silver/Gold layers).
• Partner with business and data stakeholders to translate requirements into canonical models, ontologies, and data products.
• Define and enforce data modeling standards, naming conventions, and domain-driven design principles.
• Guide teams on data ingestion patterns, transformation frameworks (e.g., dbt), and performance optimization in Snowflake.
• Integrate data management capabilities including Data Quality using SODA, Data Governance, Metadata Management, and Data Observability.
• Ensure platform scalability, security, cost optimization, and compliance with enterprise standards.
• Provide technical leadership and mentorship to data engineers and modelers.
• Act as a key contributor in roadmap planning, technical decision-making, and stakeholder communication.

Required Skills & Experience

• 10+ years of experience in data engineering and platform architecture, with at least 3+ years in a lead role.
• Strong hands-on experience with Snowflake (performance tuning, clustering, security, cost optimization).
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)
• Understanding with Data Domains (Client, Finance etc.)
• Deep understanding of data modeling (dimensional, canonical, domain-driven).
• Experience designing or working with ontology / semantic layers (business vocabularies, relationships, metrics).
• Strong knowledge of modern data stack tools (dbt, orchestration, CI/CD for data).
• Experience implementing Data Quality, Data Governance, Metadata, and Data Observability solutions.
• Experience with orchestration tools such as: Apache Airflow, Prefect, or Luigi.
• Solid SQL skills and strong understanding of ELT patterns.
• Ability to lead cross-functional teams and communicate complex concepts to both technical and non-technical stakeholders.

Nice to Have

• Experience with knowledge graphs, semantic models, or graph technologies.
• Exposure to enterprise data platforms supporting multiple domains and global users.
• Background in cloud-native architectures and large-scale data modernization programs.