Senior Data Analyst, Omada Health

• At least 5+ years of deep expertise in marketing analytics
• 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
• Track record of working on multiple projects under aggressive deadlines
• Bachelor’s degree in Computer Science, Engineering, or Mathematics
• Skills: Data Analysis and Visualization, Amplitude, Tableau, SQL, ETL, Databases, Data model and Excel
• Your work will identify new ways we can drive engagement and clinical outcomes for our members, supporting new products and features and demonstrating the value of existing development. Your insight is critical to the continuous improvement of our program, processes and team
• A highly motivated, self-driven professional who loves to focus on storytelling through data and solving complex data challenges

Desirable

• The Member Growth Analytics team is responsible for developing our portfolio of reports and data insights that support the Growth Marketing team seeking to enhance and improve our outreach efforts
• Using data spanning campaign engagement, landing page and application interactions, channel insights and test results, you will partner our marketing team to identify and solve the biggest challenges across Omada’s 1.5k customers and 600k members
• Own the data architecture, data mapping and reporting to support Growth Marketing needs for campaign and channel insights
• Deliver key performance insights to support the development of member marketing campaigns across email, direct mail, digital, and client-led channels
• Own data tagging, test sizing and statistical analysis for A/B and multivariate campaign and creative tests
• Develop and support well-designed Tableau dashboards (or similar BI tools)
• 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 product analytics use cases