Business Operations Associate (Data Lead)

Daybreak•Los Angeles, CA

12h

About The Position

This is a Business Operations role where you will be Daybreak's point person for all things data. The ideal candidate will treat data as a lever for making the business smarter by driving efficiency, exposing gaps, shifting teams from reactive to proactive, and equipping everyone at Daybreak to move faster and decide better. You will own the full spectrum: the infrastructure and delivery of data and analytics, but also support through data science, operational problem-solving, stakeholder management, financial analysis, and AI-powered enablement across the company. This role is a hybrid of Business Operations, Finance, Analytics, and Data Engineering. Daybreak owns an end-to-end patient journey dataset that is almost unique in sleep medicine, and your mandate is to turn it into a durable advantage. This is a high-visibility role with direct exposure to leadership, genuine ownership of your domain, and wide flexibility in how you get there. You'll work closely with our Head of Business Analysis and partner daily with Commercial, Sales, Marketing, Customer Experience, Clinical Operations, Finance, Product, and Engineering.

Requirements

3-5+ years in Business Operations, Strategy & Analytics, GTM/Analytics Engineering, consulting, or a similar role where you owned both the analysis and the outcome.
Advanced SQL and strong proficiency in Python (or R) are a must. You're hands-on and self-sufficient with data.
Strong Excel and PowerPoint skills are a huge plus.
Working fluency with the modern data stack (dbt, Snowflake or comparable cloud warehouses, ELT tooling, BI platforms), with enough depth to own it end to end.
Demonstrated use of AI tools in real workflows. AI is already part of how you work day to day.
Strong business judgment: you instinctively connect a metric to a P&L line, a process, or a decision.
Excellent stakeholder management: you can align a room of operators, clinicians, and executives, and communicate technical concepts without the jargon.
A strong sense of ownership and accountability for outcomes.
Bachelor's degree in Math, Statistics, Economics, Business, Physics, Philosophy, Computer Science, Engineering, or another analytical discipline.

Nice To Haves

Startup or high-growth operating experience.
Prior analytical roles, preferably working with large datasets to drive insights and impact decision-making.
Building agentic or LLM-powered workflows (e.g., automations, internal tools, analysis copilots) preferred.
Healthcare, digital health, medical device, or other regulated industries a plus.
GTM engineering or revenue operations tooling such as enrichment, outbound automation, CRM data architecture (e.g., Salesforce, Apollo, Clay) a plus.

Responsibilities

Act as a strategic thought partner to leadership: frame ambiguous business questions, structure the analysis, and drive to a clear recommendation.
Analyze acquisition, conversion, retention, operational throughput, unit economics, and revenue trends to identify the next constraint on growth.
Partner with Finance on forecasting, pipeline reconciliation, commissions, and the metrics that connect operational performance to financial outcomes.
Run cross-functional initiatives end to end: define the problem, build the model, align stakeholders, ship the change, and measure the result.
Own the technical foundation for analytics company-wide: data modeling and transformation in dbt and Snowflake, ingestion via Snowflake or other API integrations, and the reporting layer on top.
Keep the platform trustworthy: monitor pipelines, resolve issues, manage alerting, and establish data governance, metric definitions, and documentation so there is one version of the truth.
Build dashboards, executive scorecards, and reporting frameworks that give every function visibility into its own performance and equip leadership with visibility across all of them.
Continuously improve data quality, accessibility, and speed-to-answer across the organization.
Treat AI as a core part of the job: use LLMs and agentic workflows to automate analysis, accelerate reporting, and compress the time from question to answer.
Borrow from the GTM engineering playbook: build enrichment, scoring, routing, and automation workflows that make our commercial and outbound motions materially more effective.
Enable the rest of the company: build self-serve tools, templates, and workflows so teams can answer their own questions.
Identify where AI can remove manual work across operations, then build it, ship it, and teach people to use it.
Build predictive models, segmentation, survival/cohort analyses, and experimentation frameworks where they change a real decision: lead prioritization, conversion, retention, and capacity planning.
Write advanced SQL and use Python and/or R to automate analyses and push past what BI tools can answer.
Translate complex, messy datasets into clear narratives that both technical and non-technical stakeholders act on.