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.