The Role

The Employee Experience Insights & AI Enablement Team is looking for a Senior Insights Analyst to sit at the intersection ofAIstrategy,IT operations, and enterprise analytics. This is not a traditional BI role. You will build the analytics foundation that helps Morningstar27s Central Technology team understand, govern, and accelerate our AI investments while simultaneously driving operational intelligence across IT service delivery.

You will own the design and delivery of analytics platforms that span ITSM performance, AI cost governance, andknowledgeworker productivity measurement. You will be embedded in a team that is actively deploying agentic AI infrastructure (LiteLLM, AWS Bedrock, Claude/Anthropic),andmigrating to cloud-native data platforms (Snowflake, Microsoft Fabric).Additionally,identifymetrics to measure theeffectiveness of the Employee Experience Insights and AI Enablement Team.

If you are energized by building from scratch, thrive in ambiguity,comfortable with afast movingenvironment,and want your analyticswork todirectlyshapehow a global company runs its AI strategythis role is for you.

WhatYou27llOwn

1. AI Cost Analytics & Spend Governance

• Develop executive-facing dashboards (CEO/CFO-level) that connect AIspendtrends to strategic outcomes, surfacing anomalies and efficiency signals in a self-service format.

• Evolve andmaintainMorningstar27s AI cost analytics platform currently built onLiteLLM, Snowflake, andLangSmithinto a scalable, production-grade observability system.

• Build and own the architecture that integrates AI gateway telemetry (LiteLLM) with enterprise data platforms (Snowflake, Microsoft Fabric) to enable per-team, per-application, and per-modeltokenattribution.

• Partner with Cloud Services (AI Infrastructure), InfoSec, and Finance to ensure spend governance models areaccurate, auditable, and aligned to enterprise reporting standards.

2. ITSM Analytics & Operational Intelligence

• Design, build, andmaintaindashboards and datasets that drive IT service delivery performance across the Global Service Desk, endpoint operations, and employee experience functions.

• Normalize and integrate data from ServiceNow (ITSM/HRSD), DEX Performance Analytics, collaboration tools (e.g., Poly Lens), and other operational sources into cohesive, analysis-ready datasets.

• Identifypatterns, bottlenecks, and opportunities in service delivery data and translate findings into actionable recommendations for leadership.

• Respond to ad-hoc data requests from IT and business stakeholders with speed and clarity.

3. AI Enablement Opportunity Identification

• Analyze IT and business operations data to proactivelyidentifyareaswhereAI automation, agentic workflows, or LLM-based tooling can drive measurable efficiency gains.

• Build andmaintaina pipeline of data-backed AI enablement opportunities, prioritized by estimated ROI, complexity, and strategic alignment.

• Partner with the Agentic Front Door program and Central Technology leaders to quantify the impact of deployed AI solutions and feed findings back into the roadmap.

4.KnowledgeWorker Productivity Measurement

• Design and implement an analytics framework to measure the productivity impact of AI investments on knowledge workers across Morningstar.

• Define, instrument, and track meaningful productivity KPIs going beyond adoption metrics to capture time savings, task deflection, output quality, and employee sentiment.

• Build the data infrastructure needed to collect, normalize, and report productivity signals across AI tools (Claude, Copilot, Gemini, Bedrock-based agents) and employee segments.

• Produce regular productivity impact reports for senior and executive audiences, enabling data-driven decisions on AI investment prioritization.

5. Analytics Platform Architecture

• Design a scalable, modern analytics architecture that integrates Snowflake, Microsoft Fabric,LiteLLM, ServiceNow, and other enterprise data sources into a unified analytics layer.

• Champion the migration from static legacy reports to dynamic, interactive, AI-assisted dashboardsleveragingPower BI, Fabric, and agentic tooling whereappropriate.

• Define data standards, integration patterns, and governance practices that allow the analytics platform to scale as new AI tools and data sources are added.

• Evaluate and recommend analytics tooling to ensure the platformremainsbest-in-class and aligned to Morningstar27s enterprise technology strategy.

What You Bring

Required

• Proven experience in data analytics, reporting, and dashboarding with a portfolio that includes both operational (ITSM or similar),AIreporting,and strategic (executive-facing) use cases.

• Hands-on experience with Snowflake and/or Microsoft Fabric for data pipeline design, transformation, and analytics delivery.

• Proficiencywith Microsoft Power BI for dashboard development and stakeholder-facing reporting.

• Experience integrating data from APIs,CSVfiles,event streams, and diverse SaaS platforms into analysis-ready datasets.

• Strong analytical and communication skillsableto translate complex data into clear, executive-ready narratives.

• End-to-end ownership of the data solution lifecycle: requirements, design, development, deployment, and ongoing iteration.

• Comfortoperatingindependently in a fast-moving environment with competing priorities and evolving requirements.

• Collaborative mindset with experience working across technical and non-technical stakeholders.

Preferred

• Experience withLiteLLM,LangSmith, or similar LLM observability and cost tracking platforms.

• Familiarity with AI/LLM cost structures,token economics, model pricing,andusage attribution.

• Experience building AI-assisted or agentic analytics solutions, including tools such as Claude Code.

• Working knowledge of ServiceNow data structures in ITSM and/or HRSD.

• Background in productivity measurement, workforce analytics, or digital employee experience (DEX) platforms.

• Exposure to enterprise AI platforms including Anthropic/Claude, OpenAI, Google Gemini, or AWS Bedrock.