Role Description 

As an Agile Business Analyst (AI Native), you will own and drive the metrics that matter for our engineering teams: building dashboards and presentations, adding commentary and context, observing and reporting trends, and recommending new metrics as our ways of working evolve with AI and agentic software development. You will also gather, organize, analyze, and present business information to support better decisions across Product Management and PTO workflows. You will use business intelligence, process automation tools, SQL, Python, R, ETL pipelines, REST APIs, DOMO, Power Platform, RPA, and approved AI-enabled tools to improve productivity, data clarity, process documentation, and UAT quality. 

AI gives you leverage. You bring judgment. You remain accountable for validating outputs, protecting sensitive information, and delivering analysis that is accurate, practical, and aligned to business needs. 

Key Outcomes 

• Deliver business analysis outputs that improve process clarity, decision quality, and stakeholder alignment. 

• Build and maintain engineering metrics dashboards, presentations, and reporting that help leaders focus on key items, trends, and outliers. 

• Add clear commentary and context that explains what the metrics mean, why they moved, and what action to consider. 

• Recommend new or refined metrics as engineering ways of working evolve with AI-assisted and agentic software development. 

• Use AI-enabled tools responsibly to accelerate documentation, analysis, testing, and communication. 

• Support automation opportunities through practical assessment, design support, and validation. 

• Ensure UAT, data review, and process recommendations are grounded in evidence. 

Roles & Responsibilities 

• Initiate, plan, and design business processes and make recommendations to improve and support business activities. 

• Leverage business process automation tools to develop and maintain business process automation. 

• Use business intelligence systems and data to expose and resolve business issues in an effective and productive manner. 

• Build and maintain engineering metrics and leadership dashboards in DOMO to enable decision-making. 

• Prepare recurring metrics presentations for engineering and product leadership, adding commentary and color on performance, trends, and outliers. 

• Monitor engineering metrics such as delivery flow, cycle time, throughput, quality, and predictability, and report meaningful trends and risks. 

• Propose new metrics, and retire low-value ones, as teams adopt AI-assisted and agentic development practices. 

• Support and troubleshoot REST API integrations that feed metrics data, including authentication, pagination, rate limits, and failed refreshes. 

• Gather information from end users and document business processes. 

• Review PTO compliance to business processes and work with teams to improve compliance as appropriate. 

• Assist on special projects by reporting, analyzing, and making recommendations to the team. 

• Conduct User Acceptance Testing sessions to ensure deliverables meet business needs. 

• Analyze large quantities of data to support system integrity. 

• Develop SQL queries, Python or R scripts, and ETL data pipelines, including DOMO DataFlows, to source, transform, and validate metrics data. 

• Design and build Power Platform or RPA automations. 

• Use approved AI-enabled tools to support requirements analysis, documentation, dashboard narratives, UAT scenario generation, automation analysis, and stakeholder communication. 

• Validate AI-assisted outputs for accuracy, completeness, data integrity, compliance fit, and business relevance. 

• Protect confidential, customer, production, proprietary, credential, and restricted data when using AI-enabled and business tools. 

Education 

Bachelor’s Degree or equivalent in Computer or Business Informatics related field. 

Experience (years included) 

Two (2)+ years of related experience. 

Skills 

• Business analysis, process documentation, BI reporting, and dashboard development capability. 

• Experience with AWS QuickSight or other BI reporting tools such as DOMO. 

• Proficiency in Microsoft Office, particularly Microsoft Excel. 

• Proficiency in SQL and ability to develop and manage ETL data pipelines. 

• Working proficiency in Python and/or R for data analysis and automation. 

• Working knowledge of REST APIs, with the ability to support and troubleshoot API-based data integrations. 

• Hands-on experience supporting DOMO, including datasets, DataFlows, connectors, and dashboards. 

• Solid understanding of software development and agile practices, and how engineering teams measure delivery. 

• Ability to turn data into clear narrative commentary and presentations for leadership. 

• Ability to design and build Power Platform or RPA automations. 

• Ability to manage multiple tasks while effectively focusing on priority issues. 

• Ability to work as a member of an agile team. 

• Written and verbal communication skills. 

• Responsible use of approved AI-enabled tools to improve productivity, quality, and clarity. 

 
Abilities 

• Work on problems of moderate scope that are often varied and routine. 

• Review situations or data across a variety of factors and determine appropriate action. 

• Exercise judgment within defined procedures and practices. 

• Communicate analysis, recommendations, risks, and tradeoffs clearly. 

• Balance speed, accuracy, stakeholder needs, compliance, and business impact. 

 
Tools & Technologies 

Microsoft Office, Microsoft Excel, AWS QuickSight, DOMO or similar BI tools, SQL, Python, R, ETL tools, REST APIs and API testing tools, Microsoft Power Platform, RPA tools, agile collaboration tools, UAT tools, documentation tools, and approved AI-enabled productivity and analysis tools. 

AI Usage and Accountability Requirements 

• Use approved AI-enabled tools to support requirements analysis, documentation, reporting, UAT, process review, automation assessment, and stakeholder communication. 

• Review and validate AI-generated summaries, recommendations, documentation, tests, queries, workflow suggestions, and analysis before use. 

• Do not expose confidential, customer, production, proprietary, credential, or restricted data through AI tools unless explicitly approved. 

• Escalate inaccurate, incomplete, biased, insecure, or unclear AI outputs. 

• Maintain human ownership of assigned analysis, stakeholder recommendations, data interpretation, and final deliverables. 

 
Success Measures / Accountable Outcomes 

• Business analysis outputs are accurate, clear, and aligned to stakeholder needs. 

• Dashboards, reports, process documentation, and recommendations support practical decisions. 

• AI-assisted work is reviewed, validated, and used responsibly. 

• UAT activities produce clear evidence of business readiness. 

• Risks, blockers, data issues, and compliance concerns are surfaced early.