About Teal Health:

Teal Health is on a mission to provide women with the tools, access, and resources they need to make their own, informed decisions regarding their health—starting with cervical cancer screenings. We’ve created the first FDA authorized at-home cervical cancer screening. We’re replacing the in-office pap smear with a cervical cancer screening that is comfortable, convenient, and designed for women. Teal Health’s solution includes a patented Teal Wand collection device and a modern telehealth platform to make it easy for women to speak with a doctor, screen at home and understand their results.

Our Values:

We boldly champion the future women deserve. And we do this through our values, which are to elevate women, expect exceptional, and learning, every day. The Teal team lives our values and uses them to guide our decisions when building Teal.

Why we are hiring for this role:

Teal is looking for a data operations analyst to establish the foundation of a reliable and scalable data ecosystem. You’ll play a critical role in ensuring that our data is accurate, accessible, and actionable by building the systems and processes that transform raw data into clear, digestible insights.

We’re seeking an experienced data operations leader who thrives on ownership and is eager to drive data initiatives from strategy through execution. In this role, you’ll design and implement data workflows, enforce best practices around quality and governance, and deliver reporting that empowers teams across the company to make confident, data-driven decisions. You’d be joining a small but mighty team eager to profoundly change women’s health!

Role expectations & responsibilities:

• Love data, love asking questions of the data and have an operations and business mindset to use data to drive business decisions.

• Own the end-to-end data ecosystem — from pipeline reliability and governance to reporting and insights delivery.

• Lay the groundwork for scalable insights by building the first reporting structures, including self-serve dashboards, and ensuring teams have access to accurate, timely data.

• Translate data into impact by reporting business outcomes across the organization, surfacing actionable insights, and making recommendations that move company objectives and KPIs forward.

• Lead data reviews and syncs with leadership to align on metrics, insights, and business outcomes.

• Create and maintain a six month roadmap for data infrastructure, tooling, and analytics priorities, including resourcing needs.

• Have an operator’s mindset—focused on delivering accurate, actionable insights that directly impact business outcomes.

• Thrive in ambiguity and like building things from scratch.

Success in the first 12-18 months:

• Conduct a full audit of Teal’s data stack to ensure that the correct data is being captured in the product and across the various tools (Google Analytics, HubSpot, Healthie, Stripe, Quickbooks, etc.) we are using.

• Set up, configure and manage a data analytics platform to create visibility into the data across tools and across the product lifecycle via dashboards (with funnels for customer conversions and key user journey steps) as well as unique data reports (for appointments, kit tracking, etc.).

• Develop and manage data pipelines and dashboards for end-to-end marketing insights—from originating sources via Google Analytics and ad platforms (e.g., Meta, Google Ads, LinkedIn) to platform data — to create a full-funnel view of the lead-to-customer journey and campaign performance.

• Work with the executive team to define key metrics and create automated dashboards for easy visibility into the state of the business.

• Create custom reports upon request to help solve issues as they arise.

• Identify and drive operational improvements by surfacing areas of high volume activity, operational inefficiencies, high cost activities, or bottlenecks to scale. Quantify and prioritize improvements based on business and financial impact.

• Develop and evolve Teal’s data dictionary, establish governance processes, and operationalize data by automating recurring reports (team meetings, investor updates, and board prep).

• Document what’s been built, including identifying areas where data ops or analytics engineering support is needed.

Qualifications:

• Have 3–5 years in data analytics, data science, business intelligence, or data operations.

• Is fluent in SQL and can write clean, modular, and production-ready queries.

• Proficiency with Python, R, and other data science programming languages and related libraries.

• Experience with data analysis and visualization tools like Looker and/or PostHog and with integrating external data sources with these tools.

• Demonstrated ability to translate data insights into business recommendations or product decisions.

• Can communicate tradeoffs between technical constraints and business needs.

• Familiarity with experimentation techniques (e.g., A/B testing, power analysis, hypothesis testing).

• Previous experience and willingness to work across distributed teams.

• Experience working with healthcare or patient-facing data (e.g., EHRs, lab results, appointment flows, claims, or device data), including understanding of PHI and HIPAA-compliant data practices.

• Familiarity with startup operational models and velocity (e.g., weekly metrics reviews, GTM dashboards).

• Bachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Economics, Engineering, or similar).

Benefits offered include:

Equity Compensation

HSA / FSA

401K

Parental leave for eligible employees

Flexible PTO