Data Operations Engineer

Data Operations Engineer ABOUT SONDERMIND At SonderMind, we believe everyone deserves one personalized, connected, and effective mental health destination to take care of their mental health and well-being at any stage of life. SonderMind care encompasses everything from therapy and medication management to meditation and mindfulness exercises. Our clinicians leverage our digital tools and research to deliver increasingly high-quality care and to develop thriving practices. Combining technology and human connection, SonderMind drives better outcomes through our comprehensive approach. Learn more about SonderMind at sondermind.com or download the mobile app, available on iOS and Android. 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 https://www.instagram.com/sondermind/?hl=en, Linkedin https://www.linkedin.com/company/sondermind/, and Twitter https://twitter.com/SonderMind?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor. ABOUT THE ROLE As a Data Operations Engineer, you are at the heart of SonderMind’s data rebuild — helping us move from a fragmented, reactive data environment to one that is stable, governed, and built for AI-first consumption. In this role, your core focus is the reliability and performance of our Snowflake Business environment. You’ll monitor and optimize Snowflake compute, manage resource usage, and make sure our core data assets are consistently available and fresh. You’ll work closely with our Data Engineering team to understand the data in Iceberg and with our Analytics Engineers on schema design, RBAC modeling, and dbt transformation logic, bringing a reliability and performance perspective to a team that genuinely values that lens. SonderMind moves fast and priorities evolve — you’ll thrive here if you’re someone who adapts quickly and sees a shifting landscape as an opportunity rather than an obstacle. You’ll collaborate day-to-day with AI Semantic Engineers who are building Snowflake Cortex-powered interfaces, meaning the work you do directly shapes how the broader organization accesses and understands its data. WHAT YOU’LL DO - The Iceberg Bridge: Own the execution and monitoring of data pipelines moving from the Data Engineering Iceberg warehouse into the Snowflake Business environment. - Staging Layer Excellence: Architect and build a high-quality staging layer in Snowflake. Implement robust data validation and cleaning processes at the point of entry to ensure downstream reliability. - Snowflake Sovereignty: Manage Snowflake schemas, roles, and warehouse configurations. Implement a "Least Privilege" RBAC model aligned with Data Governance Team to secure business data while enabling high-velocity access. - Pipeline Stability: Monitor, troubleshoot, and optimize all downstream data pipelines. Your goal is to eliminate data failures in the refined layers and ensure 99.5% availability for core data assets. - Performance Engineering: Continuously audit and tune Snowflake performance. Identify and resolve bottlenecks in data processing to maintain sub-second response times for AI interfaces and dashboards. - dbt Partnership: Work side-by-side with Analytics Engineers to optimize dbt transformation logic, ensuring the "Source of Truth" is built on the most efficient and reliable staging tables possible. - AI-First Foundation: Structure data schemas specifically for AI consumption, ensuring that our AI Agent Analysts have clean, well-indexed, and logically partitioned data to power Snowflake Cortex. - External Data Integrations: Partner with the Manager of AI Data Enablement to automate data pipelines for external partners and payors. WHAT DOES SUCCESS LOOK LIKE? - Ramp-up (90 Days): Successful takeover of all Snowflake staging and role management; full documentation of the Iceberg-to-Snowflake pipeline; zero critical failures in the refined data stack. - Continued Success: Achiev ... (truncated, view full listing at source)