Job Description Summary Help EnterpriseKC create meaningful 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 Data Intelligence Analyst combines business and data analysis, performance intelligence, and hands-on AI-enabled building to move analytical work from a clearly framed question through useful first use, measurement, and continued improvement. The role strengthens EnterpriseKC's teams and programs by providing analytical support for their work serving partners across government, enterprise, academia, and economic development.
The Data Intelligence Analyst should wake up every morning thinking about where EnterpriseKC can create the most value; what the evidence reveals; what analysis, intelligence, or decision tool would make the greatest difference; how the work can strengthen DataSphere and create reusable capabilities; and whether it is being used, what value it is creating, and what should improve or come next.
Job Description
Ownership:
- Framing priority business and operating questions as clear analytical questions with defined objectives, intended value, and measures of success.
- Conducting business and data analysis - including performance intelligence - that reveals patterns, implications, risks, opportunities, and evidence-based recommendations.
- Personally designing and building useful dashboards, analytical prototypes, AI-enabled tools, and other intelligence solutions that support decisions and action.
- Strengthening and growing DataSphere through valuable data, measures, methods, models, workflows, use cases, and reusable analytical capabilities.
- Evaluating the use, decision value, performance, and outcomes of assigned analytical work; improving what is built and carrying reusable learning into future work.
Key Responsibilities:
- Provide analytical support to teams and programs across EnterpriseKC by translating the priority business and operating questions they identify - including through their work with partners across government, enterprise, academia, and economic development - into analytical questions, intended value, measures of success, and practical paths forward.
- Find, organize, analyze, and interpret quantitative and qualitative information; define relevant baselines, leading and lagging indicators, and measures of success; and identify patterns, gaps, trends, risks, and implications.
- Turn analysis into clear findings, recommendations, and decision support, not simply reports of activity.
- Personally build dashboards, prototypes, AI-enabled tools, and other intelligence solutions with analytics, visualization, business-intelligence tools, low-code or no-code tools, and other appropriate resources.
- Test assigned 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 across future work.
- Explore and test new analytical approaches, tools, and decision experiences when they can create meaningful value.
- Monitor adoption, outcomes, changing needs, and feedback; identify what should improve; and carry what is learned into the next assignment, tool, or capability.
General Responsibilities:
- Partner closely with the Data Storytelling & Insights Specialist on shared assignments so analytical utility, meaningful insight, human context, and the audience experience reinforce one another, while independently leading other analytical and intelligence assignments.
- Collaborate with product, project, data, research, design, communications, technology, and subject-matter contributors to bring the work together.
- Report to the Pillar 8 Leader and 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.
Expected Outcomes:
- Teams across EnterpriseKC and the partners they serve across government, enterprise, academia, and economic development gain useful analysis, recommendations, and decision tools that help them solve problems, choose paths forward, and move faster.
- Important questions and opportunities are evaluated through credible evidence rather than activity, assumption, or technology alone.
- DataSphere becomes more useful, connected, and widely used as EnterpriseKC adds valuable analytical capabilities and builds on what works.
- Assigned work connects objectives, leading and lagging indicators, performance information, and outcomes in ways that support continuous improvement.
- Analytical methods, measures, workflows, intelligence capabilities, and learning from one assignment strengthen work across EnterpriseKC teams, programs, and strategic industry clusters, helping EnterpriseKC extend proven analytical work without sacrificing quality.
Key Metrics:
- Assigned work begins with a clear business or operating question, analytical question, intended value, and measure of success.
- Quality, credibility, timeliness, and decision value of the resulting analysis, findings, and recommendations.
- Time from an identified need to useful analysis, a working prototype, or meaningful first use.
- Adoption and repeat use of dashboards, intelligence tools, and DataSphere capabilities; task completion; and reported usefulness in making a decision or advancing work.
- DataSphere improvements, reusable analytical capabilities, and increased use that can be traced to real needs.
- Reviews of analytical performance and decision value that result in documented recommendations, improvements, or reusable learning.
- Work completed on time and accepted within agreed review rounds.
Attributes:
- A hands-on analyst and builder who is energized by turning ambiguous questions into useful intelligence and practical tools.
- Relentlessly focused on creating value rather than producing activity, reporting, or technology for its own sake.
- Curious and disciplined about how organizations operate, what the evidence reveals, and what data and emerging technology can make possible.
- Entrepreneurial and resourceful, while able to challenge assumptions, distinguish activity from outcomes, and communicate uncertainty honestly.
- Accountable for the quality of the analysis, the usefulness of what is built, and what happens after delivery.
Skills and Abilities:
- Translate priority questions identified by EnterpriseKC teams and programs - including questions arising from their work with partners across government, enterprise, academia, and economic development - into analytical questions, decision requirements, and practical work plans.
- Find, evaluate, organize, analyze, and interpret quantitative and qualitative information; define objectives, baselines, leading and lagging indicators, outcome measures, and analytical methods.
- Identify patterns, gaps, trends, scenarios, implications, risks, and opportunities, then turn them into clear recommendations.
- Define what an intelligence solution should help people do and guide the analytical work from question through testing in use, evaluation, and evidence-based improvement.
- Build usable prototypes and intelligence solutions with artificial intelligence, analytics, visualization, business-intelligence tools, low-code or no-code tools, and reusable workflows.
- Communicate analysis, findings, possibilities, recommendations, risks, and decision value clearly to people with different levels of data experience.
- Lead analytical projects and cross-functional work, coordinate contributors, and move complex assignments forward.
- Evaluate use, decision value, performance, and outcomes; apply the learning to DataSphere, reusable capabilities, and future work.
- Use artificial intelligence and emerging tools creatively and responsibly; advanced full-stack programming is not required, but the willingness and ability to build are essential.