Business Intelligence Analyst Specialist

The Business Intelligence Analyst Specialist, hereafter referred to as Worker, who meets the general qualification of Business Intelligence Analyst Specialist and the specifications outlined in this document for the above-named customer.

Responsibilities

• Requirements and Testing
  • Work with program areas and project sponsor to gather business requirements and translate into technical specifications.
  • Lead as Test Manager creating test plans and test cases that integrate from Agile sprint cycles, epics and stories, as well as Waterfall project plans and all project artifacts.
  • Translate complex data requests and operational requirements into clear, actionable queries for AI against complex analytics data sources.
  • Explain technical findings and data limitations in simple, non-technical language to end-users.
• AI Prompting and Data Synthesis
  • Develop and refine effective AI prompts and query strategies to retrieve and synthesize data accurately from complex datasets.
  • Guide non-technical users in crafting precise prompts to get the data they need, ensuring fidelity and accuracy.
  • Develop a library of standardized prompts and query templates for common reporting needs.
• Data Reporting and Visualization
  • Extract, integrate, and analyze data from multiple complex internal and external sources to support program needs.
  • Collaborate with end-users and performance analysts or IT internal leaders to create and validate reports, dashboards, and data visualizations for program monitoring and official reporting.
  • Provide subject matter expertise on validating output from AI, particularly with respect to identifying and mitigating hallucinations.
  • Ensure all data outputs adhere to agency reporting standards, data governance, and compliance regulations.
• Data Literacy and Training
  • Champion data literacy across the organization by developing and conducting AI training sessions for non-technical staff.
  • Create clear, comprehensive documentation and tutorials on using AI tools for data synthesis.
  • Promote a data-driven culture by enabling and empowering all employees to effectively utilize data and AI.
• Collaboration and Problem-Solving
  • Work closely with data engineering, IT, and Program teams to troubleshoot data-related issues and address inconsistencies and mitigation strategies.
  • Provide expert guidance to program staff on interpreting data trends and answering complex data questions.
  • Stay up to date on new AI and data analytics tools and techniques to continuously improve data access, data quality, and reporting.