Data Intelligence Analyst

EnterpriseKC•Leawood, KS

About The Position

The Data Intelligence Analyst helps EnterpriseKC create value by turning priority business questions and trusted evidence into intelligence, recommendations, decision tools, and reusable capabilities that improve decisions and performance, while strengthening DataSphere, EnterpriseKC’s shared intelligence platform. The role combines business and data analysis, performance intelligence, and hands-on AI-enabled building to take analytical work from a clearly framed question through useful first use, measurement, and continued improvement. It provides analytical support to EnterpriseKC teams and programs serving partners across government, enterprise, academia, and economic development. The analyst manages an assigned portfolio of analytical work, intelligence solutions, projects, and priorities, reporting to the Pillar 8 Leader. They partner with the Data Storytelling & Insights Specialist on shared assignments and collaborate with product, project, data, research, design, communications, technology, and subject-matter contributors. The role includes protecting sensitive information, using artificial intelligence responsibly, and preserving the definitions, sources, methods, assumptions, and limitations behind important findings and recommendations. Success means delivering credible, timely, useful analysis and decision tools; testing and improving solutions based on evidence and feedback; and making DataSphere more useful, connected, widely used, and reusable across EnterpriseKC’s teams, programs, and strategic industry clusters.

Requirements

Ability to translate priority business and operating questions into analytical questions, decision requirements, and practical work plans.
Ability to find, evaluate, organize, analyze, and interpret quantitative and qualitative information.
Ability to define objectives, baselines, leading and lagging indicators, outcome measures, and analytical methods.
Ability to identify patterns, gaps, trends, scenarios, implications, risks, and opportunities and turn them into clear recommendations.
Ability to define what an intelligence solution should help people do and guide work through testing, evaluation, and evidence-based improvement.
Ability and willingness to build usable prototypes and intelligence solutions with artificial intelligence, analytics, visualization, business-intelligence tools, low-code or no-code tools, and reusable workflows.
Ability to communicate analysis, findings, possibilities, recommendations, risks, and decision value clearly to people with different levels of data experience.
Ability to lead analytical projects and cross-functional work, coordinate contributors, and move complex assignments forward.
Ability to evaluate use, decision value, performance, and outcomes and apply learning to DataSphere, reusable capabilities, and future work.
Ability to use artificial intelligence and emerging tools creatively and responsibly.
Advanced full-stack programming is not required; willingness and ability to build are essential.

Responsibilities

Frame priority business and operating questions as analytical questions with clear objectives, intended value, and measures of success.
Conduct business, data, and performance analysis to reveal patterns, implications, risks, opportunities, and evidence-based recommendations.
Find, organize, analyze, and interpret quantitative and qualitative information; define baselines, leading and lagging indicators, and success measures.
Turn analysis into clear findings, recommendations, and decision support rather than simply reporting activity.
Personally design and build dashboards, analytical prototypes, AI-enabled tools, and other intelligence solutions using appropriate analytics, visualization, business-intelligence, low-code or no-code tools, and other resources.
Strengthen DataSphere by connecting valuable data, measures, models, methods, workflows, use cases, and analytical capabilities for reuse.
Test analytical and intelligence solutions in use; evaluate their decision value, performance, and outcomes; and refine the analysis, tool, or method based on evidence and feedback.
Explore and test new analytical approaches, tools, and decision experiences when they can create meaningful value.
Monitor adoption, outcomes, changing needs, and feedback; identify improvements and apply learning to future assignments, tools, and capabilities.
Partner with the Data Storytelling & Insights Specialist on shared assignments while independently leading other analytical and intelligence assignments.
Collaborate with product, project, data, research, design, communications, technology, and subject-matter contributors.
Manage an assigned portfolio of analytical work, intelligence solutions, projects, and priorities with strong follow-through.
Protect sensitive information, use artificial intelligence responsibly, and preserve the definitions, sources, methods, assumptions, and limitations behind important findings and recommendations.