Digital Success Analytics Engineer

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. About the Team The Digital Success Analytics team powers decisions for Digital customer experience success at Salesforce. We are becoming a truly data-driven enterprise. That means trusted, governed metrics, world-class self-serve analytics, and AI-ready insights that Customer Success teams use every day. We own the vision, strategy, and execution of our internal Analytics and BI portfolio for Digital Success. Our analytics help teams improve the digital success experience, drive product adoption and customer satisfaction, and reduce attrition. Our portfolio includes dashboards and scorecards, canonical metric definitions, experimentation and measurement frameworks, predictive and ML models, and self-serve, AI-assisted analytics. We partner with IT Data Engineering and Platform teams, who own the underlying pipelines and infrastructure. We act as an internal consulting partner to CS leaders, taking complex questions through to measurable business impact. Roles and Responsibilities • Deliver decision-grade insights: Analyze customer, case, chat, voice, and in-app behavior to uncover what drives resolution, deflection, adoption, CSAT, and attrition. Turn findings into clear recommendations for leaders. • Measure and compare product performance: Design and run launch-tracking, A/B and cohort analyses, and comparative assessments (for example, AI agent vs. legacy experiences). Define consistent KPIs and methodology so results are trusted and comparable. • Build predictive and statistical solutions: Develop models for forecasting, segmentation, propensity, anomaly detection, and root-cause analysis. Take them from prototype to production use. • Define metrics and the “North Star”: Help define the North Star and success metrics for the DS portfolio. Move beyond vanity metrics to the signals that correlate with long-term growth. Support target setting and show impact and ROI. • Champion a single source of truth for metrics: Establish canonical metric definitions and a common language across all customer success fuctions such as - Digital & Assisted support, Training, Certifications, Success Guides, and Architects. Work with Data Engineering to define requirements and validate golden datasets. • Build dashboards and data stories: Create Tableau dashboards, scorecards, and visualizations that turn analysis into narrative for stakeholders up to executive level. • Enable self-serve and AI-augmented analytics: Build frameworks, semantic definitions, and enablement (Tableau, MCP, and LLM-based tools) so users can explore trusted data with minimal friction. • Data instrumentation and measurement strategy: Define what data must be captured to measure product success. Specify tracking requirements with product and engineering partners. • Data governance and responsible AI: Apply privacy standards (GDPR/CCPA) in analysis and ensure ethical use of AI/ML models. • Partner and influence: Work with business stakeholders to prio