Senior Snowflake Engineer

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols.
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets.
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.).
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information.

Role and Responsibilities:

• Design, develop, and maintain scalable data pipelines and ELT/ETL workflows on Snowflake, integrating data from diverse internal and external sources.
• Architect and optimize Snowflake data models, schemas, and warehouses for performance, reliability, and cost efficiency.
• Implement and enforce data governance, security, role-based access controls, and data quality standards across the platform.
• Monitor and tune warehouse performance, query execution, and resource consumption to control costs and meet SLAs.
• Build and maintain CI/CD pipelines for data infrastructure using tools such as dbt, Git, and orchestration frameworks (e.g., Airflow).
• Leverage advanced Snowflake features — Snowpipe, Streams, Tasks, Time Travel, Dynamic Tables, and Snowpark — to deliver near-real-time and automated data solutions.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into robust data solutions.
• Mentor junior engineers, conduct code reviews, and establish best practices for data engineering across the team.

Required Qualifications:

• 5+ years of data engineering experience, with 3+ years of hands-on Snowflake development in a production environment.
• Expert-level SQL skills, including complex query design, optimization, and performance tuning.
• Strong experience designing dimensional and/or data vault data models and building ELT pipelines.
• Proficiency with dbt for data transformation and modeling.
• Hands-on experience with at least one cloud platform (AWS, Azure, or GCP) and its data services.
• Programming proficiency in Python for data processing, automation, and scripting.
• Proficiency using GenAI and AI-assisted development tools (e.g., Claude Code, ChatGPT/OpenAI, GitHub Copilot, Cursor) to accelerate coding, debugging, and documentation.
• Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
• Solid understanding of data warehousing concepts, data governance, and security best practices.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

• SnowPro Core or Advanced certification.
• Experience with Snowpark, Snowflake Cortex, or building data applications on Snowflake.
• Familiarity with streaming technologies such as Kafka or Kinesis.
• Experience with infrastructure-as-code tools (Terraform) and containerization (Docker, Kubernetes).
• Background supporting BI and analytics tools (Tableau, Power BI, Looker).
• Experience working in regulated industries or with sensitive data (PII, PHI, financial data).
• Experience building data or analytics solutions with LLMs and GenAI frameworks — prompt engineering, AI agents/copilots, and tools such as Snowflake Cortex, OpenAI APIs, or LangChain.