• Lead complex technical implementations, shaping data architecture and transformation pipelines that prioritize performance, maintainability, and reusability. • Drive adoption of best practices for dbt development, testing, and CI/CD while mentoring peers through code reviews, pairing, and technical guidance. • Contribute to internal tooling, shared dbt packages, and scalable frameworks that raise the standard of analytics engineering across teams and clients. • Partner with analysts, engineers, and stakeholders to translate complex and often ambiguous business requirements into well-structured data models and semantic layers. • Serve as a technical and strategic advisor in cross-functional planning—balancing stakeholder requirements, data governance, and long-term scalability. • Model strong communication and mentorship, helping other AEs and cross-functional teammates develop clarity and confidence in data architecture decisions. • Lead end-to-end delivery of analytics engineering workstreams, ensuring solutions are reliable, well-documented, and aligned with client objectives. • Anticipate and address systemic data quality or modeling issues, implementing durable solutions that improve trust and efficiency across the stack. • Enable sustainable project delivery by optimizing performance, reducing technical debt, and contributing to shared standards that scale across teams.