• Design, build, and maintain core data pipelines and infrastructure that power analytics, experimentation, machine learning and AI fine tuning and agentic use cases across product, clinical, and operations teams. • Own data architecture decisions related to ingestion, transformation, storage, and serving layers, with a focus on scalability, reliability, cost efficiency, and maintainability. • Establish and enforce data quality, observability, and reliability standards, including SLAs, monitoring, alerting, and incident response practices for critical datasets. • Partner closely with analytics, data science, and product engineering teams to understand data needs and translate them into well-designed, reusable data models and pipelines. • Lead technical initiatives and drive best practices across the data engineering team, including code quality, testing, documentation, and data contracts. • Ensure data systems meet privacy, security, and compliance requirements, with a strong understanding of handling sensitive mental health and healthcare data. • Mentor and support other data engineers, providing technical guidance, design feedback, and helping raise the overall engineering bar. • Support other responsibilities and ad-hoc projects as needed based on evolving business and platform needs.