Data Intelligence Analyst

EnterpriseKC•EnterpriseKC Leawood, KS

About The Position

The Data Intelligence Analyst turns 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, taking analytical work from a clearly framed question through useful first use, measurement, and continued improvement. The position provides analytical support to EnterpriseKC teams and programs serving partners across government, enterprise, academia, and economic development. It manages an assigned portfolio of analytical work, intelligence solutions, projects, and priorities, and reports to the Pillar 8 Leader. The analyst partners closely with the Data Storytelling & Insights Specialist on shared assignments and collaborates with product, project, data, research, design, communications, technology, and subject-matter contributors. The role is accountable for the quality and usefulness of its analysis and solutions, including evaluating adoption, decision value, performance, and outcomes after delivery. It also contributes useful data, measures, methods, models, workflows, use cases, and reusable analytical capabilities to DataSphere. The analyst protects sensitive information, uses AI responsibly, and preserves the definitions, sources, methods, assumptions, and limitations behind important findings and recommendations.

Requirements

Ability to translate priority business and operating questions into analytical questions, decision requirements, and practical work plans.
Ability to evaluate, organize, analyze, and interpret quantitative and qualitative information and 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 analytical work through testing, evaluation, and evidence-based improvement.
Ability and willingness to build usable prototypes and intelligence solutions with AI, 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.
Use AI and emerging tools creatively and responsibly.
Advanced full-stack programming is not required; willingness and ability to build are essential.

Responsibilities

Translate priority business and operating questions into clear analytical questions, intended value, measures of success, and practical work plans.
Find, organize, analyze, and interpret quantitative and qualitative information; define baselines, indicators, and measures of success; and identify patterns, gaps, trends, risks, and implications.
Turn analysis into clear findings, evidence-based recommendations, and decision support.
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 resources.
Test analytical and intelligence solutions in use, evaluate their decision value and performance, and refine the analysis, tool, or method based on evidence and feedback.
Strengthen DataSphere by connecting useful data, measures, models, methods, workflows, use cases, and analytical capabilities for reuse.
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 carry learning into 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 AI responsibly, and document definitions, sources, methods, assumptions, and limitations behind important findings and recommendations.