Manager Business Intelligence

The Manager, Business Intelligence owns the day-to-day delivery of business intelligence, reporting, and analytics work across the BI/AI function. Acting as a primary liaison between the business community and the onshore and offshore development teams, this role gathers and documents business requirements, defines the scope of each request, directs the team to deliver against agreed priorities, and serves as the advocate for business users within the IT department. This is a delivery, coordination, and people-leadership role that translates direction into clear, actionable work, keeps delivery moving, and ensures quality and timeliness. Strong communication and the ability to discuss solutions with both technical and business audiences are central to the role, as is a working command of the BI and AI toolset the team operates. 1. Business liaison - Serve as a primary point of contact between the business community and IT for all BI, reporting, and analytics requests. Build expert knowledge of Klein Tools business applications and maintain frequent contact with stakeholders. 2. Requirements & scope - Gather and document business requirements, define the scope of each request (outputs, data sources, acceptance criteria), and hand off clear, unambiguous specifications to the development team. 3. Team direction & priorities - Direct and coordinate day-to-day work across the onshore and offshore BI team; assign tasks, set and manage priorities, remove blockers, and ensure on-time delivery against agreed timelines. 4. AI agent & AI-enabled delivery - Coordinate delivery of AI agent and AI-enabled analytics initiatives - scoping requirements, tracking progress, and aligning the team with the function’s AI roadmap and the Agentic Register. 5. Leadership & coaching - Provide day-to-day leadership, guidance, and coaching to BI developers, ensuring work follows the standards and technical direction set by the Director. 6. Management communication - Communicate with all levels of management regarding information needs, and discuss solutions effectively with both technical and business communities. 7. Backlog & pipeline management - Maintain the BI backlog and delivery pipeline; track and report on progress, throughput, and team capacity, and drive backlog burn-down as request volume grows. 8. Documentation - Oversee the creation and maintenance of application, user, and training documentation for BI and analytics solutions, and support user enablement. 9. Release & upgrade coordination - Coordinate and communicate BI application releases and system upgrades, including aligning data warehouse activities during upgrades of other IT systems. 10. Delivery & data-load monitoring - Monitor delivery quality and routine data loads in partnership with the team; escalate issues to the Director and ensure timely resolution. 11. Source analysis support - Support source-system analysis and data-profiling efforts by coordinating the team and clarifying business rules and data issues with stakeholders. 12. Continuous improvement - Identify needs and opportunities for improved data management and delivery, and recommend them to the Director for prioritization. 13. User advocacy - Maintain strong relationships with business users and act as their advocate within IT, managing expectations across competing demands. 14. Continuing education - Stay current with BI and AI tools - including AI coding assistants - and promote their responsible, productive adoption to improve team efficiency. Job Qualifications Education · Bachelor’s degree in Information Systems, Computer Science, Business, or a related field - or equivalent practical experience. Experience · 8+ years in business intelligence, reporting, or data-delivery roles, with 2+ years coordinating or leading projects, teams, or vendors. Experience with distributed onshore/offshore teams preferred. Required Knowledge & Skills · Communication - exceptional written and verbal skills; comfortable eliciting requirements directly from business users and translating between business and technical audiences. · Business analysis - able to gather, structure, and document requirements so the team can execute without rework. · Project & people coordination - proven ability to manage concurrent requests, set priorities, and direct a team to deliver on time. · BI fundamentals - solid working understanding of SQL, data warehousing concepts, ETL, reporting, and OLAP cubes - enough to scope work, brief developers, and validate deliverables. Required Technical Knowledge The Manager is not expected to architect or build within these tools, but must carry working familiarity across the team’s stack - enough to scope and estimate requests, brief and direct developers, review deliverables, and speak credibly with users and the team: · Data integration & ETL - SQL Server Integration Services (SSIS) and modern pipeline patterns (e.g., Azure Data Factory, Fabric pipelines). · Reporting & analytics - Power BI (reports, dashboards, datasets, data-model basics) and SQL Server Reporting Services (SSRS) for paginated/operational reporting. · Modeling - SQL Server Analysis Services (SSAS) tabular models and OLAP cubes. · Data platform - SQL Server (read and follow SQL, schema conventions), Microsoft Fabric (lakehouse/warehouse and Power BI integration concepts), and relevant Azure services (storage, data, identity). · Programming & apps - Python (recognize and scope its use in data processing, automation, and AI workflows) and a general understanding of internal web applications that surface analytics. · Delivery practices - version control (Git / Azure Repos) and deployment methodology - environments (dev/test/prod), releases, and basic CI/CD concepts. Preferred (a Plus) · AI agents - familiarity with AI agent concepts and agentic patterns (e.g., retrieval-augmented generation, tool/API use, LLM APIs), and the ability to scope and coordinate AI agent delivery alongside traditional BI work . · AI coding assistants - exposure to AI coding assistants (e.g., GitHub Copilot, Claude Code) and how they accelerate development, testing, and documentation. · Hands-on depth - prior practical experience in any part of the stack above (e.g., authoring Power BI reports, writing SQL, or building AI agents) beyond a functional understanding. · Domain - exposure to data from ERP / order-management / supply-chain systems in a manufacturing or distribution business. · Machine learning & predictive analytics - familiarity with ML concepts and predictive modeling in a business context, particularly exposure to use cases such as sales forecasting or sentiment analysis; ability to coordinate delivery of ML-driven solutions alongside traditional BI work, including engaging with data scientists or model developers and translating outputs into actionable dashboards and reports. · Tooling & certifications - experience with ticketing / work-tracking systems, standard PM practices, and relevant Microsoft data, AI, or project certifications.