{"id":2723723,"url":"https://alion.io/job/salesforce-digital-success-analytics-engineer","title":"Digital Success Analytics Engineer","company":{"id":70,"name":"Salesforce","domain":"salesforce.com","url":"https://alion.io/company/salesforce","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":433,"ghost_share":0.014,"stale_share":0.594,"repost_share":0.058,"time_to_fill_p50_days":22,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":103000,"max_usd":198000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1721},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentforce","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"GDPR","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-10-16T15:43:16Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T06:08:10Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nJob Category\nSoftware EngineeringJob Details\nAbout Salesforce\nSalesforce 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.\nReady 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.\nAbout the Team\nThe 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.\nWe 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.\nRoles and Responsibilities\nDeliver 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.\n 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.\nBuild predictive and statistical solutions: Develop models for forecasting, segmentation, propensity, anomaly detection, and root-cause analysis. Take them from prototype to production use.\nDefine 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.\nChampion 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.\nBuild dashboards and data stories: Create Tableau dashboards, scorecards, and visualizations that turn analysis into narrative for stakeholders up to executive level.\n 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.\nData instrumentation and measurement strategy: Define what data must be captured to measure product success. Specify tracking requirements with product and engineering partners.\n Data governance and responsible AI: Apply privacy standards (GDPR/CCPA) in analysis and ensure ethical use of AI/ML models.\nPartner and influence: Work with business stakeholders to prioritize high-value analytics opportunities. Collaborate with IT Data Engineering, Architecture, Platform, and Governance, and with Analytics & Data Science partners.\nOperate with rigor: Work in agile delivery with clear documentation, and share best practices through storytelling and enablement.\nQualifications\nEducation & Experience\nBachelor’s or Master’s degree in Computer Science, Statistics, Data Science, AI, Information Technology, or a related field.\n7-8 years of experience in analytics, as a Data Analyst, Analytics Engineer, Data Scientist, or BI Engineer.\n Management consulting experience (a plus): Experience at a consulting firm, or in an internal strategy or consulting function, structuring ambiguous problems and turning findings into executive-ready recommendations.\nTechnical Expertise\nAdvanced SQL (Snowflake) and Python for analysis, modeling, and automation.\nStrong statistical foundation, including probability distributions, hypothesis testing, confidence intervals, p-values, and experiment design. You can tell a genuine trend from statistical noise.\nExperience building predictive or ML models (regression, classification, clustering, time series, anomaly detection).\nProficiency in Tableau and other BI tools.\nFamiliarity with the Salesforce ecosystem.\nGit and basic CI/CD for reproducible, production-ready analytics.\nWorking knowledge of data modeling concepts (pipelines, medallion architecture). Deep pipeline engineering is not required.\nAnalytical Skills\nFind insights and build predictive tools and visualizations for storytelling.\nApply a consulting-style approach: structure ambiguous problems, run hypothesis-driven analyses, prioritize by impact, and present clear “so what” recommendations.\nTranslate complex AI and data science research into practical, actionable solutions.\nThink strategically about emerging technologies.\nCommunicate findings clearly to technical and executive audiences.\nThe “Modern” Analyst (AI Literacy)\n Augmented analytics: Use AI tools (Claude Code, Gemini, etc.) to automate cleaning and spot anomalies in large datasets.\nPrompt engineering for data: Use LLMs to write and debug complex SQL and Python.\nAgentic AI skills: Build agentic workflows and skills that automate reporting, such as scorecard refreshes and self-serve querying.\nNice to Have\nMaster’s or PhD in Statistics, Data Science, Information Systems, Computer Science, or a related field. An MBA is also welcome.\nManagement consulting experience in strategy, analytics, or transformation.\nExperimentation and causal inference experience.\nFamiliarity with metadata management, data catalogs, and data discoverability.\nAI/ML product development in an enterprise context.\nUnleash Your Potential\nWhen you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance andbe your best, and our AI agents accelerate your impact so you cando your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what’s possible - for yourself, for AI, and the world.\nAccommodations\nIf you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.\nPlease note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.\nPosting Statement\nSalesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications - without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.\nIn the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. 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