Sr Lead AI Engineer - I_Snowflake

Location: Remote (Working hours: PST timezone) REQUIREMENTS (please call these out in your notes) SQL: Highly Skilled. Must be familiar with common table elements (CTEs), complex joins, and analytic & windowing functions. Python: Some experience is required. We prefer Intermediate or higher skill. Snowflake: Prefer candidates with real-world experience using Snowflake. Data Profiling: Must be able to identify unique and primary keys and determine cardinality, data type, precision, and scale, and skew. Effective communication skills to communicate internally within our team and externally across other teams, notably Product and QA Understands the scope and context of the work and takes initiative to achieve the goals, rather than waiting to take specific direction. “Desirements” (please call these out in your notes) Healthcare Industry Experience: Some experience is desired, preferably Payer experience. Data Modeling Experience desired. Python: Some experience desired. Specific technologies to be used and level of proficiency? SQL: Highly skilled to expert level. Python: Some experience is desired with a preference for intermediate or higher skill. DBT: Prefer candidates with real-world DBT experience. Snowflake: Prefer candidates with real-world experience using Snowflake. Day to day Job Description of a Senior Data Engineer role: Design and implement scalable data models and pipelines that align with business objectives Develop complex DBT models using advanced features (macros, packages, custom materializations) Collaborate with business leaders to translate complex requirements into technical solutions Identify and resolve performance bottlenecks in Snowflake queries and DBT models Optimize Snowflake warehouse configurations and query performance Design and implement data quality frameworks and testing strategies Create reusable DBT packages and macros for team-wide use Manage CI/CD pipelines for DBT deployments Review and approve pull requests from junior team members Troubleshoot production issues and implement long-term solutions Document architectural decisions and data lineage