About the Role The Sr. Principal Data Analyst is the strategic analytics lead for Cornerstone University, translating complex business and learning data into actionable recommendations for leaders. This role owns executive reporting, KPI design, data stewardship, and predictive insights that improve operational effectiveness and help teams understand customer, partner, and employee enablement outcomes. Key Responsibilities • Own the design and maintenance of executive dashboards, scorecards, and recurring business performance reporting. • Define and maintain leading and lagging performance indicators with business leaders, including consistent definitions, baselines, and measurement methods. • Develop advanced analyses, forecasts, and predictive models to surface trends, risks, opportunities, and potential business impact. • Build and improve Tableau dashboards and reporting solutions using Snowflake and other approved enterprise data sources. • Steward Cornerstone University data definitions, quality controls, lineage, documentation, and reporting consistency. • Partner with program, product, customer-facing, and enablement stakeholders to connect learning activity with readiness, adoption, and business outcomes. • Present clear findings, tradeoffs, and recommendations to executives; conduct focused ad hoc analyses to support decisions. • Identify, test, and implement AI-enabled automation for recurring data preparation, quality checks, and reporting; measure impact and retire unnecessary manual processes. • Apply AI-assisted analytical methods with validation of source data, model assumptions, accuracy, privacy, and human review. • Perform other duties as assigned. What Success Looks Like • Owned — Dashboard reliability: publish agreed executive reporting on schedule • Owned — KPI stewardship: document definitions, owners, sources, and quality checks for 100% of priority KPIs • Owned — AI transformation: baseline manual reporting hours in the first 60 days and reduce them by 20% within six months while retaining review controls. • Influenced — Decision impact: document where analysis informs a prioritization, readiness, or operational decision. Required Experience • Bachelor’s degree or equivalent relevant experience. • 7+ years of experience in data analytics, business intelligence, business analytics, or a related field. • Advanced SQL, business intelligence and data visualization skills; strong Excel capabilities. • Experience developing executive dashboards, defining KPIs, and translating complex analysis into recommendations for senior leaders. • Strong data quality, analytical modeling, stakeholder partnership, and communication skills. • Ability to critically evaluate AI-generated analysis, assumptions, and data-handling risks. • Experience with Tableau, Snowflake, and enterprise data ecosystems. • Predictive analytics, forecasting, statistical methods, or data governance experience. • Experience measuring learning, enablement, customer adoption, or operational performance. • Experience implementing AI-assisted analytics or reporting automation. AI Continuous Learning • Use AI to accelerate exploratory analysis, data documentation, and reporting workflows while independently validating results and protecting sensitive information. • Uses AI tools every day in their own work and can show an example where AI changed how the work is done, not just how fast. • Evaluates AI output critically for accuracy, bias, privacy and data handling, and knows when a human must decide. • Actively explores internal (Cornerstone AI) and external tools, brings recommendations to the team, and retires tools and processes that do not earn their place. • Keeps skills current through communities, certifications, hands-on experimentation and industry learning, and shares what they learn with the team. How You Work • Customer obsessed: Start with the learner’s and the account’s problem, not the tool. • Innovate and transform: Challenge the status quo and retire systems and processes that do not earn their place. • Proactive leadership: See problems and act before being asked. • Get it done: Ship, measure and iterate. • Forward looking: Plan for where technology and the business are going. • AI native: Use AI daily to rethink work, not just accelerate it. • Get uncomfortable to succeed: Take on hard, ambiguous problems and stay with them.