Digital Success Analytics Engineer

Salesforce•Austin, TX

21h

About The Position

The Digital Success Analytics Engineer works on Salesforce’s Digital Success Analytics team, which powers data-driven decisions and customer experience success. The team owns the vision, strategy, and execution of its internal analytics and BI portfolio, including dashboards and scorecards, canonical metric definitions, experimentation and measurement frameworks, predictive and machine-learning models, and self-serve, AI-assisted analytics. It partners with IT Data Engineering and Platform teams, which own the underlying pipelines and infrastructure, and acts as an internal consulting partner to Customer Success leaders. The role analyzes customer and product experience data to improve resolution, deflection, adoption, customer satisfaction, and attrition outcomes. It develops decision-grade insights and recommendations, measures product performance, builds predictive and statistical solutions, defines trusted success metrics, and creates dashboards and self-serve analytics capabilities. The position also works with product, engineering, business, data, and governance partners to establish instrumentation requirements, validate datasets, apply privacy standards, and deliver measurable business impact.

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 analysis, prioritize by impact, and present actionable recommendations.
Ability to translate complex AI and data science research into practical solutions and communicate findings to technical and executive audiences.
Experience using AI tools to automate data cleaning and identify anomalies, using LLMs to write and debug complex SQL and Python, and building agentic workflows to automate reporting and self-serve querying.

Nice To Haves

Master’s or PhD in Statistics, Data Science, Information Systems, Computer Science, or a related field; an MBA is also welcome.
Management consulting experience in strategy, analytics, or transformation, or experience in an internal strategy or consulting function.
Experience with experimentation and causal inference.
Familiarity with metadata management, data catalogs, and data discoverability.
Experience developing AI/ML products in an enterprise context.

Responsibilities

Analyze customer, case, chat, voice, and in-app behavior to identify drivers of resolution, deflection, adoption, CSAT, and attrition, and provide clear recommendations to leaders.
Design and run launch tracking, A/B tests, cohort analyses, and comparative product assessments; define consistent KPIs and methodologies.
Develop forecasting, segmentation, propensity, anomaly-detection, and root-cause-analysis models, moving them from prototypes into 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, and work with Data Engineering to define requirements and validate golden datasets.
Create Tableau dashboards, scorecards, and visualizations that communicate analytical findings to stakeholders, including executives.
Build frameworks, semantic definitions, and enablement using Tableau, MCP, and LLM-based tools to support trusted self-serve analytics.
Define data capture and tracking requirements with product and engineering partners to measure product success.
Apply GDPR and CCPA privacy standards in analysis and ensure ethical use of AI and machine-learning models.
Partner with business stakeholders to prioritize high-value analytics opportunities and collaborate with IT Data Engineering, Architecture, Platform, Governance, and Analytics & Data Science partners.
Deliver work rigorously in an agile environment, document work clearly, and share best practices through storytelling and enablement.