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.
Individual pay is determined by many factors, including experience, relevant education or training, and organizational needs. The mid-range to maximum of the salary range is generally reserved for individuals who are highly experienced in the role.