• Own the end-to-end data lifecycle, from ingestion and transformation to delivery, ensuring alignment with business priorities and analytical goals. • Architect and oversee scalable, high-performance data pipelines and warehouse structures that serve as the foundation for company-wide analytics and automation. • Partner with Engineering and Product teams to establish data design standards, naming conventions, and quality benchmarks, ensuring long-term sustainability and performance. • Drive decisions on data architecture, tooling, and storage optimization, balancing scalability, cost, and performance tradeoffs. • Lead data modeling efforts that transform complex, cross-domain data into clear and accessible models supporting business-critical dashboards, experimentation, and AI workloads. • Implement robust data validation frameworks and monitoring systems to proactively detect and resolve data quality issues. • Champion data governance — define standards for access control, lineage, cataloging, and compliance. • Collaborate with security and platform teams to institutionalize best practices for data stewardship and democratized analytics. • Partner with senior business stakeholders to identify high-impact analytical opportunities and translate business questions into scalable data solutions. • Use strong command of SQL, Snowflake, and Python to design complex analytical models and build self-serve dashboards in Tableau or Looker. • Develop and deliver insight frameworks that enable data-informed decision-making across Marketing, Product, and Operations teams. • Mentor analysts and engineers, fostering an environment of learning and technical excellence. • Continuously evaluate and implement emerging data tools and frameworks to enhance speed, accuracy, and reliability of analytics delivery. • Lead initiatives to automate reporting workflows and implement CI/CD for data pipelines, ensuring reproducibility and scalability. • Partner cross-functionally to identify and eliminate data bottlenecks, optimizing for both velocity and reliability in decision support. • Drive a culture of proactive problem-solving through documentation, standardization, and data enablement across teams.