Everpure, Inc. (Everpure Data) is seeking a Data & Analytics Manager, Product Analytics to lead high-impact analytics initiatives for our clients in the Information Technology & Software space. In this role, you'll own key product metrics, experimentation frameworks, and data products that guide roadmap and feature decisions. You'll partner with product and engineering teams to design robust data models, build scalable dashboards, and perform deep-dive analyses on user behavior, funnels, and engagement. You will translate complex data into clear narratives and recommendations, while mentoring analysts and championing best practices in analytics, experimentation, and cloud data platforms in our collaborative, learning-focused culture.
Responsibilities
- Lead end-to-end product analytics initiatives to inform strategy and roadmap decisions for Everpure, Inc. clients
- Define and track key product metrics, experimentation frameworks, and success criteria across digital products
- Partner with product, engineering, and business stakeholders to translate questions into analytic approaches and actionable insights
- Design and build scalable analytics pipelines, dashboards, and self-service reporting using modern BI and data tools
- Apply statistical analysis and A/B testing to evaluate feature performance, user behavior, and funnel optimization opportunities
- Collaborate with data engineering teams to ensure data quality, reliability, and well-modeled product data sources
- Communicate findings via clear narratives, data visualizations, and recommendations to both technical and non-technical audiences
- Mentor analysts and contribute to best practices, documentation, and reusable analytic assets
- Stay current on analytics, experimentation, and AI/ML techniques to enhance Everpure Data's product analytics offerings
- Support pre-sales and client workshops by framing analytics use cases and demonstrating solution capabilities
Required Skills
- SQLProduct analytics tools (e.g., Amplitude, Mixpanel, Heap, Pendo)
- A/B testing and experimentation design
- Python or R for data analysis
- Data modeling for analytics
- Dashboarding and BI (Tableau, Power BI, or Looker)
- Statistical analysis and hypothesis testing
- Data storytelling and stakeholder communication
- Cloud data platforms (AWS, GCP, or Azure)
- Working with event-based and user behavioral data