Seeking a Senior Data Engineer with extensive experience in modern ELT and transformation engineering, particularly leveraging DBT for enterprise data layers. This role involves architecting and implementing robust data solutions.

The ideal candidate will possess a deep understanding of DBT project standards, modular design, testing, and deployment strategies. You will play a key part in building and optimizing data models for critical business domains.

Design and implement staging, core, and mart layers using standard dbt patterns. Develop and maintain comprehensive dbt tests to ensure data quality and integrity. Implement snapshots, seeds, macros, and reusable components for efficiency. Collaborate with Snowflake developers to optimize physical data structures for dbt models. Ensure functional equivalence between legacy and new dbt outputs through collaboration with functional teams. Participate in performance tuning of dbt models and Snowflake queries. Integrate dbt with CI/CD pipelines for automated runs and validations. Contribute to the documentation of dbt models, data lineage, and business rules. Engage in defect analysis, bug fixes, and enhancements during migration and stabilization phases.

A minimum of 10 years in modern ELT/transformation engineering with strong DBT-based development expertise for curated enterprise data layers is required. Proven experience in defining DBT project standards, modular model design, testing frameworks, documentation, macros, environment strategy, and deployment controls is essential. Demonstrated ability to lead the development of conformed Silver layer models across Policy, Claims, Billing, and shared dimensions/reference domains. Significant experience integrating DBT with Databricks/Delta Lake, orchestration tools, version control, and automated quality checks. Capability to guide developer productivity, establish code review standards, promote reusability, conduct performance tuning, and ensure traceability from mapping to model implementation.