Digital Success Analytics Engineer

Salesforce•Austin, TX

About The Position

The Digital Success Analytics Engineer supports Salesforce’s Digital Success Analytics team, which provides trusted, governed metrics, self-serve analytics, and AI-ready insights for Customer Success teams. The team owns the vision, strategy, and execution of its internal analytics and business intelligence portfolio, helping improve digital customer success experiences, product adoption, customer satisfaction, and retention. Its work includes dashboards and scorecards, metric definitions, experimentation and measurement frameworks, predictive and machine-learning models, and self-serve, AI-assisted analytics. The team partners with IT Data Engineering and Platform teams, which own the underlying pipelines and infrastructure, and serves as an internal consulting partner to Customer Success leaders. The role analyzes customer and support interaction data to identify drivers of resolution, deflection, adoption, satisfaction, and attrition, then turns findings into recommendations for leaders. It measures product performance through launch tracking, A/B tests, cohort analyses, and comparisons of different experiences; develops predictive and statistical solutions; helps define success metrics and a North Star; and establishes shared metric definitions. The engineer also builds Tableau dashboards and data stories, enables self-serve and AI-augmented analytics, defines product instrumentation requirements, and applies privacy and responsible-AI standards. The position requires close collaboration across business, data, architecture, platform, governance, and analytics partners, with rigorous documentation and agile delivery.

Requirements

Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, AI, Information Technology, or a related field.
7–8 years of experience in analytics as a Data Analyst, Analytics Engineer, Data Scientist, or BI Engineer.
Advanced SQL, including Snowflake, and Python skills for analysis, modeling, and automation.
Strong statistical foundation, including probability distributions, hypothesis testing, confidence intervals, p-values, and experiment design.
Experience building predictive or machine-learning models, such as regression, classification, clustering, time series, or anomaly detection.
Proficiency in Tableau and other business intelligence tools.
Familiarity with the Salesforce ecosystem.
Experience with Git and basic CI/CD for reproducible, production-ready analytics.
Working knowledge of data modeling concepts, including pipelines and medallion architecture; deep pipeline engineering is not required.
Ability to structure ambiguous problems, conduct hypothesis-driven analyses, prioritize by impact, and present clear recommendations.
Ability to translate complex AI and data science research into practical solutions and communicate findings to technical and executive audiences.
AI literacy, including use of AI tools to automate analytics work, prompt engineering for SQL and Python, and agentic AI workflow development.

Nice To Haves

Management consulting experience, or experience in an internal strategy or consulting function, structuring ambiguous problems and preparing executive-ready recommendations.
Master’s or Ph.D. in Statistics, Data Science, Information Systems, Computer Science, or a related field; an MBA is also welcome.
Experience in strategy, analytics, or transformation consulting.
Experience with experimentation and causal inference.
Familiarity with metadata management, data catalogs, and data discoverability.
Experience with AI/ML product development in an enterprise context.

Responsibilities

Analyze customer, case, chat, voice, and in-app behavior to identify drivers of resolution, deflection, adoption, customer satisfaction, and attrition, and provide clear recommendations to leaders.
Design and conduct launch tracking, A/B tests, cohort analyses, and comparative product-performance assessments; define consistent KPIs and methodologies.
Develop predictive and statistical solutions for forecasting, segmentation, propensity, anomaly detection, and root-cause analysis, taking solutions from prototype to production use.
Help define the Digital Success portfolio’s North Star and success metrics, support target setting, and demonstrate impact and ROI.
Establish canonical metric definitions and a common language across Customer Success functions; work with Data Engineering to define requirements and validate trusted datasets.
Create Tableau dashboards, scorecards, and visualizations that communicate analytical findings to stakeholders, including executives.
Build frameworks, semantic definitions, and enablement for self-serve and AI-augmented analytics using Tableau, MCP, and LLM-based tools.
Define data instrumentation and measurement requirements with product and engineering partners.
Apply privacy standards such as GDPR and CCPA, and ensure responsible and ethical use of AI and machine-learning models.
Prioritize high-value analytics opportunities with business stakeholders and collaborate with IT Data Engineering, Architecture, Platform, Governance, and Analytics & Data Science partners.
Work in an agile delivery environment, document work clearly, and share best practices through storytelling and enablement.
Use AI tools to automate data cleaning and anomaly detection, use LLMs to write and debug SQL and Python, and build agentic workflows that automate reporting and self-serve querying.