Data Visualization Engineer

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Full Time Remote - US, US

5 days ago Requisition

About Keystone

Keystone is one of the largest and fastest-growing insurance brokerages in the country — ranked #26 among U.S. retail brokers and #4 fastest-growing in the Top 50 by Business Insurance. Our platform spans 33+ states, 180+ agency partners, and more than $500 million in revenue — uniting some of the country's most respected independent agencies with the scale, tools, and resources of a national brokerage.

Through operational support in finance, HR, marketing, technology, producer development, and M&A, we empower agencies to grow while preserving what makes them who they are — their client relationships and their role in the communities they serve. Guided by our trademarked commitment, "Trust, our most important policy," Keystone is building the insurance solution every family and business can count on.

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Data Visualization Engineer

Position Summary:

We are seeking an experienced Data Visualization Engineer with hands-on experience building enterprise-grade business intelligence solutions and a strong passion for applying AI and Generative AI capabilities to analytics, visualization, dashboards, and reporting. The ideal candidate is highly proficient in Power BI, data modeling, DAX, Power Query, and data visualization, while also being comfortable using AI-assisted capabilities to improve developer productivity, identify insights, enhance user experiences, and enable business users to interact with data more naturally.

This individual reports to the VP of Data & Analytics and will partner closely with business stakeholders, data engineers, analysts, and analytics leaders to transform complex data into intuitive, actionable insights and scalable self-service analytics solutions.

Key Responsibilities:

• Design, develop, and maintain sophisticated Power BI dashboards, reports, scorecards, and analytical applications for business and executive audiences.
• Translate complex business requirements into intuitive and actionable analytical experiences.
• Develop scalable semantic models, measures, calculated columns, and business logic using DAX.
• Apply appropriate dimensional modeling techniques, including star schema, fact and dimension design, hierarchies, and reusable semantic models.
• Design dashboards using strong principles of data visualization, UX/UI, information hierarchy, and data storytelling.
• Optimize Power BI solutions for performance, scalability, usability, and maintainability.
• Implement drill-through, bookmarks, tooltips, dynamic measures, field parameters, conditional formatting, and other advanced Power BI capabilities where appropriate.

AI-Enabled Business Intelligence

• Use AI and Generative AI tools to accelerate dashboard and report development, requirements interpretation, DAX development, documentation, testing, and troubleshooting.
• Leverage AI-assisted analytical capabilities to help identify patterns, anomalies, trends, correlations, and business insights within enterprise data.
• Create intuitive analytical experiences that allow users to move beyond static reporting toward conversational and AI-assisted data exploration.
• Understand how to formulate effective prompts and business questions to obtain reliable analytical results from AI-powered tools.
• Explore appropriate use of natural-language querying, AI-generated narratives, automated summaries, anomaly detection, forecasting, and intelligent visualization recommendations.
• Identify opportunities where AI can reduce manual reporting effort and improve analyst and business-user productivity.
• Help establish reusable patterns and best practices for responsible AI adoption within BI and analytics.
• Stay current with advancements in AI-enabled analytics and recommend capabilities that can provide measurable business value.
• Connect Power BI to data from cloud platforms, databases, APIs, data warehouses, lake houses, Excel/SharePoint sources, and other enterprise applications.
• Collaborate with data engineering teams to define appropriate datasets and data models for analytics consumption.
• Perform data profiling, validation, reconciliation, and troubleshooting to ensure dashboard accuracy.
• Develop efficient incremental refresh and data refresh strategies.
• Understand upstream/downstream data dependencies and assist with…