nData Engineer

• At least 5 years of experience as a Data Engineer or in a similar role. \n, * Snowflake expertise: SQL, Tasks/Streams, and performance\n
• Cloud experience: Hands-on with AWS (S3, VPC, IAM, Airflow, ECS; Lambda a plus).\n
• Data modeling: Familiar with dimensional modeling, database design, and semantic layer design.\n
• ETL/ELT engineering: Experience building and optimizing data pipelines, including hands-on work with dbt for transformations.\n
• Governance & Security: Understanding of AD/LDAP integration, RBAC, and data cataloging.\n
• Programming: Proficient in SQL and Python.\n
• AI fluency: Demonstrated experience using AI tools and LLMs in day-to-day engineering work (prompting, code generation, workflow automation). Candidates should expect this to be a major focus of the interview.\n
• Strong problem-solving skills and a desire to learn emerging data technologies.\n

• Build and maintain scalable data pipelines using Python, dbt, and Snowflake-native tooling (Tasks/Streams), deployed on AWS (S3, Airflow, ECS).\n
• Assist in migrating existing Dataiku pipelines into Snowflake to simplify architecture and improve efficiency.\n
• Continuously improve workflows for performance, reliability, and maintainability.\n

\n \nSemantic Modeling & Views\n \n \n

• Develop and maintain Snowflake semantic views that support analytics and reporting needs.\n
• Apply data modeling best practices to ensure consistency across business domains.\n
• Support creation of governed, role-based semantic layers for Finance, Operations, and other enterprise areas.\n