Data Analyst

The Employee Experience Insights & AI Enablement Team is looking for a Senior Insights Analyst to sit at the intersection of AI strategy, IT operations, and enterprise analytics. This is not a traditional BI role. You will build the analytics foundation that helps client's 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, and knowledge worker productivity measurement. You will be embedded in a team that is actively deploying agentic AI infrastructure (LiteLLM, AWS Bedrock, Claude/Anthropic), and migrating to cloud-native data platforms (Snowflake, Microsoft Fabric). Additionally, identify metrics to measure the effectiveness of the Employee Experience Insights and AI Enablement Team.

If you are energized by building from scratch, thrive in ambiguity, comfortable with a fast

moving environment, and want your analytics work to directly shape how a global company runs its AI strategy this role is for you.

What You’ll Own 1. AI Cost Analytics & Spend Governance Develop executive-facing dashboards (CEO/CFO-level) that connect AI spend trends to strategic

outcomes, surfacing anomalies and efficiency signals in a self-service format.

Evolve and maintain client's AI cost analytics platform currently built on LiteLLM,

Snowflake, and LangSmith into 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-

Partner with Cloud Services (AI Infrastructure), InfoSec, and Finance to ensure spend

governance models are accurate, auditable, and aligned to enterprise reporting standards.

Design, build, and maintain dashboards 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.

Identify patterns, 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 proactively identify areas where AI automation,

agentic workflows, or LLM-based tooling can drive measurable efficiency gains.

Build and maintain a 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. Knowledge Worker Productivity Measurement Design and implement an analytics framework to measure the productivity impact of AI

investments on knowledge workers across Client project.

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.

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

dashboards leveraging Power BI, Fabric, and agentic tooling where appropriate.

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 platform remains best-in-class and

aligned to client's 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), AI reporting, and strategic (executive-facing) use cases.

Hands-on experience with Snowflake and/or Microsoft Fabric for data pipeline design,

transformation, and analytics delivery.

Proficiency with Microsoft Power BI for dashboard development and stakeholder-facing

reporting.

Experience integrating data from APIs, CSV files, event streams, and diverse SaaS platforms into

analysis-ready datasets.

Strong analytical and communication skills able to translate complex data into clear, executive-

End-to-end ownership of the data solution lifecycle: requirements, design, development,

deployment, and ongoing iteration.

Comfort operating independently in a fast-moving environment with competing priorities and

evolving requirements.

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

Preferred

Experience with LiteLLM, LangSmith, or similar LLM observability and cost tracking platforms.

Familiarity with AI/LLM cost structures, token economics, model pricing, and usage 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

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