Senior Data Analyst

Hey Future Daybreakers!

Join us on a mission fueled by these core values:

• Are united by our Mission to build a world where every young person benefits from mental health support.

• Have a high bar - we’re a small team of only A players that are hard-working and determined to win.

• Do more with less - we value efficiency and output over hours worked, and thrive working in an environment where every dollar matters.

• Value humor and fun at work - this job can be hard, and bringing humor to work can make every day more fun.

• Communicate clearly and openly - we value transparency and clear and direct communication.

Discover Daybreak: Empowering Youth, Changing Lives

Daybreak Health (http://www.daybreakhealth.com/) is creating a world where every young person benefits from mental health support. We provide tech-enabled mental health care to kids ages 5-19 and their families. We partner with public school districts to reach the most vulnerable populations, and with health insurance companies to cover the cost of care for families.

We’re a YC-backed company that raised its Seed from Maven Ventures, Series A from Lightspeed Ventures Series B from Union Square Ventures.  We’re now charting the path towards profitability by 2026 while also continuing to grow our impact rapidly - our goal is to double patients served and revenue in 2026. Our dual aim is to maximize the growth of our impact while also getting to profitability to control our own destiny.

About the Role

We’re looking for a Senior Data Analyst to take a leading role in shaping Daybreak’s analytics ecosystem. This is a high-impact position focused on both foundational infrastructure and strategic insight—supporting the transition to our new OASIS platform, enabling better decision-making across teams, and leading forecasting and reporting at scale.

You’ll be embedded in a cross-functional environment, partnering closely with Product, Ops, and Engineering. This role is ideal for someone who thrives on complexity, can navigate ambiguity, and is comfortable moving between heads-down technical work and big-picture thinking.

What You’ll Do

• Act as a strategic analytics partner to our Operations and Product teams—defining and tracking key metrics, uncovering actionable insights, and helping teams prioritize based on data.
• Lead the maintenance and evolution of our modern analytics stack, including SQL, Looker, dbt, BigQuery, and Fivetran—ensuring data quality, reliability, and scalability.
• Own and continuously improve forecasting models for supply and demand, with a focus on optimizing school program delivery and freemium-to-paid user conversion.
• Support and optimize advanced GSheets-based workflows that are central to operational and resource planning.
• Translate complex business questions into clear, actionable analyses, delivering insights and recommendations that drive strategic decisions.
• Lead cross-functional initiatives that improve our data infrastructure, enhance reporting accuracy, and increase organizational trust in metrics.
• Shape the long-term vision for analytics at Daybreak, including our transition to OASIS and a broader shift toward product-led, data-informed decision making.

What We’re Looking For

• Expert-level SQL proficiency – You can write efficient, scalable queries and are comfortable debugging and reverse-engineering complex logic across large, multi-source datasets.
• Extensive experience with Looker and LookML – You’ve built robust data models, implemented scalable data architecture, and designed user-friendly dashboards that empower teams to self-serve insights.
• Hands-on knowledge of dbt and the modern data stack – You’ve worked with tools like BigQuery, Fivetran, and dbt to transform, test, and maintain clean and reliable data pipelines.
• Strong business acumen – You understand how to translate technical output into actionable insights, and you’re comfortable partnering across product, operations, and leadership to drive data-informed decisions.
• Experience owning forecasting or planning models – You’re confident in building and reasoning through planning frameworks, even when data is sparse or assumptions are required.
• Comfort with Python – While not a software engineer, you’re able to read and debug Python scripts, especially in the context of analytics workflows like scoring models, data matching logic, or basic ML implementations.
• A track record of independent ownership