Principal Engineer - Data Visualization & Analytics

Job Description The Principal Engineer
- Data Visualization & Analytics

The Principal Engineer
- Data Visualization & Analytics is responsible for performing complex data research and analysis to support business operations. By creating data mining architectures, statistical reporting, and data analysis methodologies, this role helps identify trends in large data sets and informs business decisions.

• Enable data-driven business decisions by performing complex data research and analysis and translating findings into clear, actionable insights.
• Design scalable, trusted, and business-friendly Power BI dashboards, reports, semantic models, and data visualization solutions.
• Create and improve data analysis methodologies, data mining models, and statistical reporting approaches that identify trends, risks, and opportunities in large data sets.
• Lead complex analytics projects with limited oversight and strengthen analytics capability through governance, innovation, coaching, and adoption.

• Design, build, and maintain interactive dashboards, reports, and semantic models in Power BI.
• Perform complex data research, statistical reporting, and analysis to support business operations and decision-making.
• Create intuitive visualizations and presentations that communicate trends, risks, opportunities, and recommendations to technical and non-technical audiences.
• Define technical architecture and roadmap for analytics, AI, and digital solutions, balancing business value, scalability, usability, and maintainability.
• Develop and apply data analysis methodologies, data mining architectures, models, and protocols to identify trends in large data sets.
• Optimize dashboards and analytical products for usability, accessibility, performance, and adoption using sound visualization, DAX, and modeling practices.

• Partner with stakeholders to frame business questions, gather requirements, define KPIs, and translate needs into analytical and reporting solutions.
• Manage large analytics projects or processes with limited oversight, including scope, priorities, risks, dependencies, and delivery outcomes.
• Present insights and recommendations in a clear, actionable, and audience-appropriate manner and influence decisions through data storytelling.
• Lead small project teams, coordinate cross-functional contributors, and represent the organization in project or contractual matters when required.
• Coach, review, and delegate work to less-experienced professionals and participate in training junior staff.

• Work with enterprise data from SQL databases, Databricks, SharePoint, Excel, and other approved sources.
• Use Power BI Dataflows, semantic models, and Direct Query or Import approaches as appropriate to the use case.
• Apply advanced SQL and working knowledge of Python or R to prepare, analyze, and interpret complex data.
• Apply machine learning, predictive modeling, and Apache Spark where appropriate to complex analytics use cases.
• Research existing and emerging data science and analytics techniques and recommend practical innovations that improve business outcomes.

• Design, develop, and deploy AI-powered business solutions leveraging Generative AI, LLMs, copilots, agentic workflows, and advanced analytics capabilities.
• Develop digital products and applications using Power Platform, Dataverse, SharePoint, APIs, cloud-native services, and approved enterprise technologies.
• Lead automation initiatives utilizing Power Automate, workflow orchestration, and intelligent process automation techniques.
• Evaluate emerging AI, machine learning, and automation technologies and translate them into scalable business solutions with measurable business outcomes.
• Collaborate with business and technical stakeholders to identify opportunities for AI adoption, operational optimization, and digital transformation.
• Ensure AI and digital solutions adhere to governance, security, privacy, data quality, GxP, and Responsible AI requirements where applicable.

• Ensure data quality, documentation, consistency, lineage, access control, and adherence to analytics and reporting standards.
• Apply governance practices independently, advise teams on improvements, and help establish reusable patterns and standards.
• Continuously monitor solution effectiveness and…