• Define and drive the technical roadmap for data infrastructure, establishing architectural patterns and standards that scale across the organization • Lead the design and implementation of complex, multi-system data architectures that support business-critical operations and enable innovation (data ingestion + export and delivery) • Evaluate and champion adoption of emerging technologies and best practices in data engineering, MLOps, and GenAI • Establish data governance frameworks, quality standards, and operational excellence practices across all data workloads • Drive cross-functional initiatives that require coordination between data, product, engineering, and business teams • Architect enterprise-scale data solutions for transferring data from first and third-party applications to and from our data warehouse • Design and oversee the development of robust, scalable APIs (REST, GraphQL, gRPC) that enable data access for internal teams and external partners • Lead the evolution of event-driven and API-first data architectures that support real-time data sharing and integration • Leverage Google Cloud (GCP) tools (Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM, etc.) and services (Astronomer - Apache Airflow) to architect and bring enterprise data workloads to production • Design resilient, self-healing data systems with comprehensive monitoring, alerting, and automated remediation - and participating as part of an on-call rotation. • Lead the evolution of our data platform on Google Cloud (GCP), leveraging advanced services and optimizing for cost, performance, and reliability • Define patterns for streaming and batch data architectures that serve diverse use cases • Establish best practices for data contracts, API versioning, CI/CD, documentation, and partner integrations • Lead MLOps strategy and implementation, establishing patterns for model deployment, monitoring, and governance at scale • Architect and oversee Generative AI infrastructure, enabling rapid prototyping while ensuring enterprise-grade security, compliance, and cost management • Partner with Data Science leadership to translate research initiatives into production-ready solutions • Drive innovation in AI/ML tooling and infrastructure, staying ahead of industry trends • Mentor and guide Data Engineers at all levels, conducting design reviews and providing technical feedback • Establish engineering standards, documentation practices, and knowledge-sharing processes • Participate in hiring and onboarding processes, helping to build a world-class data engineering team • Foster a culture of engineering excellence, experimentation, and continuous improvement • Partner with product, engineering, and business leaders to align data strategy with organizational goals • Communicate complex technical concepts to non-technical stakeholders, building alignment and driving informed decision-making • Represent data engineering in cross-functional planning and architecture forums • Build strong relationships with external partners and vendors