Value Insights Manager, Gong

• Degree in Computer Science, Data Science, Business, or related field or equivalent practical experience
• Highly skilled in SQL with hands-on experience in Snowflake (or similar cloud DW) and query optimization
• Proficient with dbt for transformation, modeling, documentation, testing, and comfortable owning pipelines end-to-end
• Experience with BI Tools (Tableau/Looker/Sisense) for data visualization and to translate complex data into actionable insights
• Familiar with CI/CD for data workflows, version control systems (e.g., Git), and testing frameworks
• Strong communicator who collaborates well with technical and non-technical stakeholders and can simplify complexity for executives
• Proven ability to extract insights from sales/customer success data and connect product usage to business outcomes
• Experience with CRM systems and tools (e.g., Salesforce)

Desirable

• The Gong Revenue AI Operating System unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the world’s most successful revenue teams
• At Gong, you will join a company built on innovative products, ambitious goals, and passionate people
• You will work with a team that dreams big, moves fast, and cares deeply about the craft and about each other
• Join our Technical Value Engineering team to build data foundations that prove customer value at scale
• This role combines data engineering, analytics, and customer outcome measurement
• You'll work with one of the richest B2B datasets in the world to automate insights that drive outcome realization
• Value Storytelling and Business Impact: Partner with sales and customer success teams to build ROI models, extract KPIs from Gong data, and create compelling value stories that translate technical outcomes into measurable business results for executive stakeholders
• Data Infrastructure: Design and implement robust dbt models that transform raw Gong data into analytics-ready tables supporting outcome attribution and value realization across all customers. Architect scalable, efficient data solutions using a modern stack (SQL/Snowflake, Python, dbt, Airflow, GitHub) optimized for precise business impact tracking
• Technical Leadership and Collaboration: Work closely with product, engineering and analytics teams to align data architecture with business goals while creating and documenting methodology
• Data Quality and Governance: Implement automated tests, monitoring dashboards and data quality frameworks. Partner with Product Analytics on shared data governance and definitions
• Self‑Service and Enablement: Create data marts and parameterized queries that empower Value Engineering to self-serve complex analyses while building automated insight delivery systems that enable Customer Success to demonstrate measurable value to customers
• Communication & Process Improvement: Craft persuasive presentations and thought leadership content to communicate insights internally and externally while triaging support tickets and driving systematic improvements that reduce manual work and scale impact