Senior Data Analyst, B2B Analytics

Omada Health

4h•Remote

About The Position

The B2B analytics team is responsible for developing our portfolio of reports and data insights that support our internal teams and customers seeking to improve the health of our members. Using data spanning app engagement, clinical outcomes, behavioral health indicators, and claims, you will partner our product and engineering teams to identify and solve the biggest challenges across Omada’s 1.5k+ customers and 1M+ members.

Requirements

At least 2+ years of deep expertise in enterprise analytics, ideally 7+ years of experience
Expert SQL skills with strong experience in designing and developing new queries and reports
Experience with A/B testing and statistical measurement
Ability to analyze various data sets, and develop succinct key metrics
Strong relational database design/development skills
Excellent problem solving, interpersonal, and communication skills
Comfort managing projects and keeping track of deliverables
Track record of working on multiple projects under aggressive deadlines
Bachelor’s degree in Computer Science, Engineering, Mathematics, Economics, or other STEM field

Nice To Haves

Knowledge of additional coding languages (Python or R)
Advanced experience in data visualization
Background in healthcare and experience with PHI
Knowledge of population health metrics

Responsibilities

Own the data architecture and data mapping required to support the growing B2B reporting needs
Deliver key performance insights through regular and ad-hoc analysis to drive further investment and evolution of Omada programs
Develop and support well-designed Tableau dashboards (or similar BI tools)
Organize a cross functional team to manage workstreams related to your analytics, like Performance Guarantees or Program Incentives
Work closely with various data teams to improve data quality and standardize reporting
Define operational processes and support system enhancements that drive analytics and reporting support at scale
Collaborate across multiple teams including analytics, data science, data engineering and architecture, clinical, and product
Explore and integrate applications of AI into analytics use cases