BI Engineering and Enablement Director (Primarily Office)

American Family Insurance•Boston, MA

2d•$150,000 - $255,000•Onsite

About The Position

This position provides strategic leadership for BI Engineering & Enablement, including BI tool administration, shared metrics enablement, semantic and knowledge-layer usage practices, metadata and lineage enablement, data layer usage governance, and AI-ready analytics foundations. You will ensure enterprise users, analytical tooling, dashboards, and AI-enabled workflows operate from consistent, governed, and explainable business definitions. You will partner across business, Data Engineering, Governance, Applied AI, AI Platform Engineering, Enterprise Architecture, Information Security, Application Development, Digital Services, Infrastructure, and business operations teams to improve trust, adoption, and reuse of enterprise analytics capabilities. Metric layer, semantic layer, knowledge layer, metadata, and lineage capabilities are shared responsibilities with Data Engineering and Governance, with BI Engineering & Enablement leading enablement, adoption, BI consumption patterns, metric/semantic usage practices, and AI-consumable context. You will also coordinate BI and analytics platform partners such as Tableau and GCP.

Requirements

Demonstrated experience leading BI, analytics engineering, data enablement, semantic-layer, metrics-layer, metadata/lineage, or data product enablement teams.
Experience with BI platforms, governed metrics, semantic modeling, metadata, lineage, data governance, and analytics adoption.
Demonstrated ability to connect technical data models to business meaning and decision-making.
Experience reducing metric inconsistency and improving trust in enterprise reporting and analytics.
Familiarity with how semantic layers, metrics layers, knowledge catalogs, metadata, lineage, and governed business definitions support AI, copilots, and agentic workflows.
Experience working across IT and business domains, including Data Engineering, Enterprise Architecture, Information Security, Governance, Application Development, Digital Services, Infrastructure, Product, and business operations.
Experience coordinating BI and analytics platform partners such as Tableau and GCP.
Demonstrated ability to drive adoption, enablement, standards, and usage practices across distributed analytics and business teams.
Strong business partnership and communication skills with both technical and non-technical audiences.
Demonstrated effective people leadership, prioritization, coaching, and talent development skills.
Candidates should reside within approximately 35-50 miles of one of the following office locations: Madison, WI 53783 or Boston, MA 02110.

Nice To Haves

Relocation support is offered for eligible candidates.

Responsibilities

Lead shared metrics enablement and adoption, including enterprise practices for promoting adoption and consistent use of shared metrics in partnership with Data Engineering, Governance, and business owners.
Establish metric-layer usage expectations for grain, definitions, dimensions, ownership, reuse, and change management in partnership with the teams that own upstream data engineering and governance practices.
Enable AI-consumable BI and semantic foundations by building and governing BI foundations that can be consumed by both humans and AI systems, including metric definitions, semantic context, metadata, lineage, classification, and knowledge catalog capabilities in partnership with Data Engineering and Governance.
Ensure agents and AI-enabled workflows can retrieve trusted business context from governed sources.
Lead BI tool administration and best practices, including overseeing BI and analytical tool administration, standards, enablement, and adoption practices.
Support tool consolidation and rationalization.
Promote consistent and effective use of BI capabilities across business and technology teams.
Enable semantic and knowledge-layer usage by leading enablement and usage practices for semantic, metric, and knowledge-layer foundations that connect data assets to business meaning.
Partner with Data Engineering and Governance on stewardship, upstream ownership, lineage, metadata, and governance practices.
Enable consistent business logic for dashboards, self-service analytics, embedded analytics, copilots, and AI-enabled workflows.
Govern data layer usage for BI and analytics by establishing governance practices for how curated data layers, metric layers, semantic layers, knowledge layers, and BI assets are used.
Reduce metric drift, definitional inconsistency, duplicative reporting logic, and fragmented analytics experiences.
Advance AI-ready analytics foundations by structuring BI, metrics, semantic context, metadata, lineage, knowledge assets, and business definitions so they can support both human decision-making and AI-powered tooling.
Partner with Applied AI and AI Platform Engineering to ensure AI systems use trusted definitions and explainable business context.
Assess business and Technology readiness for AI-enabled analytics and decision intelligence, including data availability, data quality, data classification, metadata completeness, lineage visibility, process context, and business definition maturity.
Identify gaps that could limit trusted BI, self-service analytics, or AI-enabled workflows.
Manage BI and analytics platform partnerships by coordinating BI and analytics platform partner relationships relevant to the role, including Tableau and GCP.
Ensure partner capabilities are aligned to enterprise BI, metric, semantic, governance, and AI-ready analytics expectations.
Drive business enablement and adoption of shared metrics, BI best practices, and trusted analytics patterns.
Help business partners understand and use governed metrics and BI capabilities effectively.
Lead teams responsible for BI enablement, BI engineering, metric-layer enablement, tool consolidation, and related delivery functions.
Build a culture of customer focus, engineering discipline, data trust, and practical enablement.