Data Analyst, Principal

About the opportunity

As a Principal Data Analyst, you are a highly analytical, technically strong, forward-thinking problem solver who thrives on turning data into strategic insight and building modern decision products. You will play a pivotal role within the Decision Intelligence team, combining deep analytical expertise with hands-on technical skills to solve complex business problems using enterprise data, business intelligence, artificial intelligence (AI), and automation.

This is a hands-on technical role with significant visibility across the organization. You will work directly with senior leaders and cross-functional stakeholders while personally designing and building analytical solutions
- ranging from enterprise semantic models and Power BI experiences to AI-powered applications and agents. Your work will directly influence strategic decisions and help evolve how the enterprise consumes data, insights, and AI.

What you'll get to do

• Promote Data-Driven Decision-Making: Translate ambiguous business questions into analytical solutions that go beyond stated needs
  - proactively surfacing insights, root causes, opportunities, and recommended actions that guide executive strategy and operational excellence.
• Build Modern Decision Products: Personally design and build analytical products ranging from scalable semantic models and advanced Power BI experiences to AI-powered analytical applications and agents. Select the appropriate interface and technology based on the business problem rather than defaulting to traditional dashboards.
• Build AI-Powered Analytical Solutions: Design, prototype, build, and help productionize AI-enabled analytical applications and agents that combine enterprise data, semantic context, large language models (LLMs), APIs, and tools to answer business questions, generate insights, and automate analytical workflows.
• Collaborate Across the Business: Partner with leaders in designated departments and other domains to define key metrics, interpret performance results, understand business context, and connect analytics and AI solutions to financial, operational, customer, and growth objectives.
• Engineer Trusted Analytical Foundations: Build and maintain reliable data models, metric definitions, semantic models, and analytical logic. Troubleshoot data gaps and partner with data engineering and governance teams to strengthen data quality, lineage, consistency, and trust.
• Apply Practical AI and Automation: Identify high-value opportunities for AI and automation, then move beyond ideation to working solutions. Apply techniques such as tool/function calling, structured outputs, retrieval and grounding, context engineering, and agentic workflows where they provide measurable business value.
• Evaluate for Accuracy and Reliability: Develop practical approaches to test and monitor AI-enabled analytical solutions, including grounding, hallucination and accuracy testing, regression testing, observability, and human review. Understand when deterministic analytics, traditional machine learning, or an LLM-based approach is most appropriate.
• Communicate with Confidence: Synthesize complex analyses and technical solutions into clear, compelling narratives and present actionable insights, tradeoffs, and recommendations to senior leadership.

Skills and experience we value

• Bachelor's degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Business Analytics, or a related analytical field; Master's degree preferred.
• 10+ years of progressive experience in data analytics, business intelligence, analytical engineering, data science, decision science, or related technical roles, preferably in an enterprise SaaS, technology, consulting, or complex business environment.
• Strong SQL, data modeling, and analytical engineering skills, with hands-on experience building enterprise analytics solutions using platforms such as Power BI. Candidates should be comfortable working directly in Power BI when needed, including semantic models, DAX, performance optimization, advanced dashboards, and executive-facing analytical products.
• Strong hands-on Python skills and comfort working with APIs, Git, JSON, testing and debugging, notebooks, and modern development workflows used to build analytical and AI-enabled solutions.
• Demonstrated hands-on experience building AI/LLM applications beyond the use of AI productivity tools or commercial chat applications. Experience should include several of the following: agentic workflows, tool/function calling, structured outputs, retrieval/RAG, context