• Own the end-to-end design and implementation of dbt models, including the simplified 6-object architecture, parametric control layer, and plug-and-play organizational hierarchy configurations.
• Optimize existing data pipelines to improve performances keeping optimal credit utilization without impacting SLA.
• Write production-grade SQL and Python scripts for data transformation, pipeline automation, and integration with upstream and downstream .
• Instrument data pipelines with robust quality frameworks—including dbt tests, row count validation, null assertions, and referential integrity checks—to ensure metric reliability for executive reporting.
• Contribute to AI integration workstreams, including building data tables and pipeline structures that support LLM-generated insight.
• Evaluate and adopt AI-native data tooling—including Snowflake CoCo, dbt Copilot, and related capabilities—in line with the team’s AI future-readiness direction set by VP leadership.
• Collaborate with other team members to deliver the task at hand to meet the committed timelines.

Minimum Qualifications

• 4+ years of professional experience in data engineering or analytics engineering, with demonstrated ownership of production-grade Snowflake environments including query optimization, RBAC configuration, and schema design.
• Intermediate to advanced proficiency in dbt, including authoring of incremental models, macros, Jinja templating, snapshot strategy for SCDs, and dbt test frameworks.
• Expert-level SQL, including window functions, recursive CTEs, complex multi-level aggregations, and query performance profiling in a cloud data warehouse environment.
• Intermediate Python proficiency for data pipeline scripting, ETL/ELT automation, and lightweight data wrangling using pandas, fastAPI, or equivalent libraries.
• Demonstrated experience designing data architecture that supports analytical reporting at enterprise scale—including dimensional modeling, object rationalization, and parametric configuration layer design.

Preferred Qualifications

• Experience in developing pipline using coding assistance such as CoCo,Co-Pilot, Cursor etc.
• Working familiarity with major clod services such as GCP,AWS , with demonstrated ability to integrate cloud-side outputs into a Snowflake-based pipeline.
• Experience incorporating AI outputs into data pipelines—including consuming LLM API responses as structured data, feature engineering for predictive models, or building tables that support AI summary generation workflows.
• =Experience with pipeline orchestration tools such as Airflow, Prefect, or dbt Cloud job scheduling, including DAG dependency management and pipeline health monitoring.
  • Git-based development discipline, including branch management, PR workflows, and CI/CD awareness applied to dbt or pipeline codebases; experience with data observability frameworks is a plus.