Data Analyst, Principal

Dayforce

2d•$101,500 - $181,300•Remote

About The Position

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.

Requirements

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 engineering, prompt design, API integration, and evaluation frameworks.
Demonstrated track record of personally designing and building AI-enabled analytical products or agentic systems from prototype toward production. Candidates should be prepared to discuss architecture, implementation decisions, evaluation methods, failures, tradeoffs, and lessons learned from systems they personally built.
Strong understanding of semantic modeling, metric design, KPI frameworks, business definitions, data quality, lineage, and governance practices needed to provide trusted context to both enterprise reporting and AI-enabled analytical experiences.
Demonstrated ability to translate ambiguous business questions into structured analytical and technical approaches, identify root causes, quantify business impact, and determine when a problem is best solved with SQL or deterministic software, traditional analytics or machine learning, a BI experience, or an LLM/agentic solution.
Experience evaluating AI systems for accuracy, grounding, reliability, and business usefulness, including approaches such as test datasets, hallucination testing, regression testing, observability, human review, and appropriate controls for enterprise use.
Proven experience leading complex analytical or AI initiatives from concept through implementation with minimal oversight, including requirements definition, stakeholder alignment, hands-on development, testing, delivery, adoption, and ongoing optimization.
Experience partnering with senior leaders and cross-functional stakeholders to define success metrics, interpret performance trends, and connect analytical and AI solutions to financial, operational, customer, and growth outcomes.
Exceptional communication, analytical reasoning, and business acumen, with the ability to synthesize complex data and technical concepts into clear narratives, evaluate tradeoffs, prioritize work based on business value, and influence decisions at senior levels.
High degree of intellectual curiosity, ownership, technical judgment, and attention to detail. We value demonstrated building experience over theoretical familiarity; candidates should be prepared to discuss systems they personally designed and implemented.

Nice To Haves

Experience working in the Human Capital Management industry is preferred.

Responsibilities

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.