Data Engineering Lead: Source ETL & Analytics

About SonderMind

At  SonderMind,  we  know  that  therapy  works.  SonderMind  provides  accessible, personalized  mental healthcare that produces high-quality outcomes for patients. SonderMind's individualized approach to care starts with using innovative technology to help people not just find a therapist, but find the right, in-network therapist for them, should they choose to use their insurance. From there, SonderMind's clinicians are committed to delivering best-in-class care to all patients by focusing on high-quality clinical  outcomes.  To  enable  our  clinicians  to  thrive,  SonderMind  defines  care expectations while providing tools such as clinical note-taking, secure telehealth capabilities, outcome measurement, messaging, and direct booking. To follow the latest SonderMind news, get to know our clients, and learn about what it’s like to work at SonderMind, you can follow us on Instagram, Linkedin, and Twitter.

About the Role

This is a hybrid role in the Applied Science function, encompassing Data Engineering and Data Science. You will function as both a technical manager and a senior contributor to our production data warehouse. As a technical manager, you (and your team) will be responsible for the designing, building, managing - and making accessible - streaming and batched data ETL pipelines, moving data from source locations to clean production tables: Your main business stakeholder will be the Analytics and Business Operations teams. As a senior contributor, you will set the standard for code deployment and collaboration across the organization, working closely and collaborating with other data engineers and analysts. This role reports to the head of Applied Science and will have a staff seat within the department, having a voice in the planning and strategic direction of our data infrastructure.

Essential Functions

• Design, construct, install, test, and maintain highly scalable data systems that include distributed databases, data warehouses, cloud storage, etc.
• Develop and maintain databases, data processing procedures, and pipelines, with accountability for the availability of timely and clean production data
• Champion partnership and collaboration with analytics, marketing, and business operations
• Ensure timely and strategically aligned decisions and execution for data engineering relating to source data ETL and analytics
• Oversee a team of data engineers, providing mentorship and opportunities for growth
• Ensure the enablement of analytics and product experimentation plans as part of the product development lifecycle
• Identify and integrate new data management technologies and software engineering tools into existing structures to improve cost and performance
• Translate complex functional and technical requirements into detailed architecture, design, and high-performing data pipelines
• Create custom software components and analytics applications in Python and SQL, with Snowflake as the primary underlying data warehouse
• Troubleshoot data-related issues, perform root cause analysis to resolve them, and communicate changes to leadership
• Optimize and ensure end-to-end data warehouse performance

What does success look like?

Success in this role will be measured by the cost, performance, accessibility, and usability of our production data across the organization, enabling timely, data-driven, decision making. This includes maintaining high availability, ensuring optimal performance, and implementing robust security measures, as well as demonstrating the ability to collaborate effectively to define solutions with both technical and non-technical team members across data science, engineering, product and our core business functions. The successful candidate will make significant contributions to improving our data infrastructure through a combination of optimizing existing processes, or developing innovative new solutions, balan