Senior Analytics Engineer, Analytics Enablement

Fullscript

19h

About The Position

Fullscript is an industry-leading health technology company dedicated to helping people improve their health. Founded in 2011, the company's initial goal was to simplify access to trusted products for practitioners, enabling them to provide better care. This concept evolved into a comprehensive platform that supports all aspects of care, currently utilized by over 125,000 practitioners for clinical insights, lab interpretations, patient analytics, education, and access to high-quality supplements. More than 10 million patients depend on Fullscript to stay connected to their care plans and adhere to treatment. The company develops tools that enhance care by making it smarter and more human, saving time, simplifying decisions, and fostering close connections between practitioners and patients. By centralizing all necessary resources, practitioners can concentrate on their primary objective: helping people get better. Fullscript invites individuals with ideas, grit, and a passion for people to join and shape the future of care. The Senior Analytics Engineer, Analytics Enablement role aims to scale how teams at Fullscript access, use, and learn from data. The core challenge is to empower individuals across the organization to self-serve trustworthy data, reducing reliance on analysts or data scientists for every query. This involves understanding user challenges, identifying common data needs, and developing the necessary tooling, assets, workflows, and enablement practices to foster greater independence. The position focuses on enhancing data access and usage systems, ensuring teams can easily find relevant data, utilize appropriate tools, and gain confidence in answering their own questions. A significant component of this role is enablement, which includes teaching teams how to use analytics tools, locate and work with datasets, approach analysis effectively, and derive answers from available tools. This may involve creating starter assets, examples, or shared patterns, with an emphasis on building repeatable tools and processes to increase data self-sufficiency over time. This role is ideal for someone who enjoys cross-stakeholder collaboration, systems thinking, and creating practical solutions that integrate BI tooling, data infrastructure, and user training.

Requirements

5+ years of experience in analytics, business intelligence, analytics engineering, data science, or a related data role
Strong hands-on SQL skills and working proficiency in Python
Experience with BI and analytics tools such as Looker, Tableau, Power BI, or similar platforms
Experience building reusable data assets, self-serve analytics workflows, internal tooling, or reporting foundations that scale beyond ad hoc requests
Experience teaching, training, mentoring, or enabling others to use data tools and analytics resources with more confidence and independence
Strong communication skills and a consultative working style, with the ability to turn recurring business needs into practical, scalable solutions
The judgment and autonomy to manage moderately complex work independently while collaborating effectively across technical and non-technical teams

Nice To Haves

Experience with natural language or AI-powered analytics tools such as Flurry
Experience working in a modern cloud data environment
Experience partnering closely with Data Engineering or Analytics Engineering teams
Experience designing enablement programs or adoption strategies for BI tools or internal data products

Responsibilities

Partner with stakeholders across Fullscript to understand recurring data needs, self-serve gaps, and patterns that can be solved through shared tooling and reusable assets
Design and implement self-serve analytics solutions such as trusted datasets, reporting foundations, templates, and workflows that make data easier to access and use independently
Improve how data is surfaced and consumed through BI tools and internal tooling, with a focus on usability, consistency, trust, and adoption
Use SQL, Python, and BI platforms such as Looker to build scalable solutions, including AI-powered analytics tools built internally
Create and deliver training, documentation, office hours, and hands-on enablement sessions that teach teams how to use tools, find data, and analyze it effectively
Partner with Analytics, Data Science, and Data Engineering to strengthen the infrastructure, patterns, and definitions that support self-serve analytics across the business
Own projects end-to-end from discovery through implementation, including ambiguous problems where the right approach isn't obvious