• Design, build, and maintain high-performance ELT/ETL pipelines into Snowflake across Enterprise systems • Architect optimized Snowflake schemas, warehouses, and data models that support analytics, forecasting, and operational workflows • Build scalable transformation logic using SQL, Python, dbt, and other modern data tools • Own and lead Teachstone’s data governance framework, including standards, policies, operating cadences, and decision rights • Define and maintain canonical enterprise data models for accounts, customers, products, segments, and lifecycle stages • Establish and enforce field definitions, naming conventions, data lineage, and documentation standards • Lead cross-system deduplication, normalization, and data quality initiatives • Chair or co-lead an enterprise data governance council, driving alignment across Growth, Delivery, CS, Finance, and Product • Ensure governance practices are practical, adopted, and embedded into day-to-day workflows—not theoretical • Manage and develop the data engineering and reporting team, including analysts and analytics engineers as applicable • Set clear priorities, standards, and delivery expectations for data pipelines, dashboards, and reporting outputs • Balance short-term reporting needs with long-term platform and governance maturity • Coach team members on best practices in data modeling, governance, and stakeholder partnership • Establish sustainable operating rhythms for intake, prioritization, and delivery of data work • Implement validation frameworks to monitor data accuracy, completeness, and consistency • Develop automated error detection, alerting, and traceability for critical pipelines • Ensure adherence to performance, privacy, and compliance requirements • Partner with Growth, CS, Delivery, Product, Marketing, and Finance to translate business needs into governed, scalable data solutions • Coach non-technical stakeholders on data ownership, stewardship, and responsible data practices • Help leaders understand how upstream data decisions affect downstream reporting, metrics, and AI use cases • Operate effectively within a matrixed organization with shared ownership and competing priorities • Optimize Snowflake compute usage, storage strategies, and query performance • Evaluate emerging tools and technologies to strengthen the data platform • Support long-term modernization, including real-time ingestion, event-driven pipelines, and AI/ML enablement