Knowledge Governance and AI Analytics Analyst

Position Summary

The client is building a growing library of AI agents, and every one of them depends on trustworthy input and honest measurement. As a Knowledge Governance & AI Analytics Analyst on the client's BI & AI Solutions team, you will own the organization's shared knowledge base: the governed repository of policies, procedures, product content and institutional knowledge that the client's AI agents retrieve from. You will also own the KPI and ROI reporting that tells the client whether those agents provide real value.

This is a hybrid analyst role, part information steward and part performance analyst, sitting at the center of how AI gets grounded and how AI gets judged.

What You Will Own

•    The shared knowledge repository that feeds retrieval-augmented AI agents across the client's business units and brands.

•    Intake, review and governance workflows for unstructured content, including PDFs, Word docs, PowerPoints, policy documents and other source material.

•    A semantic tagging and metadata taxonomy that keeps content discoverable and machine-readable for downstream AI retrieval.

•    KPI dashboards that define and track success criteria for each AI agent in production.

•    Business value and ROI reporting that translates agent performance into terms leadership and stakeholders can act on.

Key Responsibilities

•    Manage the end-to-end lifecycle of documents in the shared knowledge base: intake, review, approval, versioning, retirement and periodic re-certification.

•    Establish and enforce governance standards for what content can be uploaded, who can approve it, and how conflicting or outdated versions are resolved.

•    Apply consistent semantic tags, categories and metadata to unstructured content so it surfaces accurately in agent retrieval and search.

•    Partner with AI engineering teams to understand how each agent consumes the knowledge base, and tune content structure and tagging for better retrieval quality.

•    Define and maintain KPI frameworks for each AI agent (accuracy, adoption, resolution rate, escalation rate, cost-to-serve, etc.) in partnership with the teams that build and operate them.

•    Build and maintain dashboards (Power BI or equivalent) that track agent performance against defined success criteria.

•    Quantify business value and ROI for AI initiatives (time saved, cost avoided, revenue influenced or risk reduced) and present findings to stakeholders.

•    Audit content quality, flag stale, duplicate or non-compliant documents, and run periodic governance reviews of the repository.

•    Document tagging standards, upload procedures and approval workflows, and train content owners across the business on how to use them.

•    Collaborate with data, analytics and AI engineering teams to align on data sources, metric definitions and reporting cadence.

Required Qualifications

•    3+ years of experience in business intelligence, data analysis, knowledge management, content governance or a related analytical role.

•    Working knowledge of SQL and comfort querying relational or cloud data warehouses to build and validate reporting.

•    Experience building dashboards and reports (Power BI, Tableau or equivalent) that translate data into business-facing insight.

•    Familiarity with document and content management systems (SharePoint or equivalent), including permissions, versioning and metadata.

•    Understanding information governance concepts: taxonomy design, metadata standards, content lifecycle and access controls.

•    Ability to define and communicate KPIs and success metrics for a new or evolving initiative, not just report on established ones.

•    Strong written and verbal communication skills, with comfort presenting findings to both technical and non-technical stakeholders.

•    High attention to detail and comfort enforcing standards and approval processes across multiple content owners.

Preferred Qualifications

•    Exposure to how AI agents, chatbots or RAG (retrieval-augmented generation) systems consume structured knowledge content.

•    Experience with semantic search, taxonomy / ontology design or enterprise content tagging standards.

•    Familiarity with AI / LLM platforms (Anthropic, Google Vertex AI, OpenAI or equivalent) and how agent quality is typically measured.

•    Experience calculating and presenting ROI or business-value cases for technology or process initiatives.

•    Background in data governance, master data management or records management.

•    Familiarity with Snowflake or another cloud data warehouse.

•    Experience partnering directly with engineering teams on requirements for a product or platform still being built.

Education & Experience

Bachelor's degree in Business, Information Systems, Data Analytics, Library / Information Science or a related field, or equivalent practical experience. Candidates with a strong portfolio of dashboards, governance frameworks or knowledge-management work will be considered favorably regardless of degree field.

Experience owning a reporting function or a content / knowledge repository, even outside of AI, is directly transferable.

How You Will Work

You will work within the client's BI & AI Solutions team, partnering closely with the AI engineers building agents, the business stakeholders requesting them, and the content owners across the organization who contribute source material. You will operate with significant autonomy in setting governance standards and reporting frameworks, while staying tightly aligned with engineering on what "good" looks like for retrieval quality and agent performance. Hybrid out of Chicago.

Why This Role

The client is a large, multi-billion-dollar national enterprise with a broad footprint of locations and decades of institutional knowledge scattered across documents, systems and teams. As AI agents get built on top of that knowledge, someone must make sure the inputs are trustworthy, and someone has to prove the outputs are worth it. This role is both: you will shape how the organization's knowledge gets organized for AI, and you will be the voice that tells leadership whether the AI investment is paying off.

Core Tools & Technologies

BI & Reporting

Power BI (or Tableau), SQL

Data

Relational and cloud data warehouses; Snowflake preferred

Content Management

SharePoint (or equivalent): permissions, versioning, metadata

AI

RAG / retrieval-augmented AI agents; Anthropic, Google Vertex AI, OpenAI

Governance

Taxonomy / ontology design, metadata standards, content lifecycle, access controls