• Build and maintain dbt models supporting NPS measurement, TAC case analytics, EBV/EDW reconciliation, and PNPS computation—including incremental models, snapshot strategies, and macro-driven parametric configurations.
• Write production-grade SQL in Snowflake for multi-level hierarchy resolution (SAV → CAV → UNIFIED_PARTY_ID), complex aggregations, and pipeline performance optimization.
• Develop Python scripts for data ingestion, ELT automation, API payload processing, and lightweight data wrangling tasks within the Customer Listening pipeline ecosystem.
• Instrument pipelines with robust data quality frameworks—including dbt tests, row count assertions, null checks, and referential integrity validations—to ensure metric reliability for VP-level reporting.
• Collaborate with BI engineers on semantic model handoffs, diagnosing and resolving data-layer issues that manifest as reporting errors in Power BI.
• Support AI integration workstreams by building well-structured data layers that feed LLM-generated insight delivery pipelines (e.g., Dynamic NPS Forecast AI Summary).
• Contribute to the team’s AI future-readiness direction by evaluating and adopting AI-native data tooling including Snowflake Cortex, dbt Copilot, and related capabilities.

Minimum Qualifications

Objective, gate-level requirements. All five must be demonstrably met.

• 4+ years of professional experience in data engineering, with demonstrated production ownership of Snowflake environments including schema design, query optimization, RBAC configuration, and cost governance.
• Intermediate to advanced dbt proficiency: authoring of incremental models, Jinja macros, snapshot strategies for slowly changing dimensions, generic and singular test frameworks, and dbt documentation practices.
• Expert-level SQL including window functions, recursive CTEs, lateral flattens, multi-level hierarchical aggregations, and query profiling in a cloud data warehouse setting.
• Intermediate Python proficiency for ELT scripting, data wrangling (pandas, numpy), and API payload ingestion—with the ability to build and maintain pipeline scripts independently.
  • Demonstrated experience designing and rationalizing data models at enterprise scale, including dimensional modeling, object consolidation, and configuration-driven architecture patterns.