AVP, Data Engineering and AI Innovation

Does this position interest you? You should apply – even if you don’t match every single requirement! We're known as an auto glass company. That's the focus of what we do. But beyond the glass, we're so much more. We'll help you build a fulfilling career and encourage you to have a life. Let us be the best place you'll ever work.

The AVP, Data Engineering and AI Innovation will provide executive leadership across Safelite's data engineering, data solutions, and AI data agent teams, setting and driving the strategic vision to build, modernize, and continuously evolve the enterprise data platform and centrally managed data assets. This business-facing technical leader will reimagine what self-service analytics and enterprise data asset management look like in an AI-enabled organization, ensuring the business is served with trusted, timely, and governed data and BI assets that confer actionable, data-driven outcomes.

The leader will build and grow high-performing teams of data engineers, BI developers, and AI/data-agent practitioners, expanding their skill sets and building cohesiveness across the function while championing innovation, collaboration, and disciplined data quality, security, and governance. The expected result is a scalable, AI-forward data organization that empowers embedded analysts and business partners.

• Set and drive the multi-year strategic vision and roadmap for enterprise data engineering and data solutions, reimagining self-service analytics and enterprise data asset management as AI-enabled capabilities that scale across the organization.
• Provide technical leadership to continually modernize the data platform, finalizing the decommissioning of legacy data integrations and technologies.
• Lead the transition to new data assets and ways of working through effective change management and sustained stakeholder engagement.
• Build, manage, and develop high-performing teams of data and analytics engineers, growing their skill sets and forging cohesiveness into a robust engine for delivering trusted data.
• Reimagine the role of the data warehouse in embedded-analyst self-service, creating governed, intuitive assets and a single source of truth that empower embedded analysts and business users across the enterprise to work responsibly within AI-native tooling.
• Champion AI innovation, piloting and scaling AI/ML and agentic capabilities (e.g., data agents) that automate data delivery, data quality, and insight generation.
• Own enterprise data asset management and contributions to the engineering-led semantic layer to enable scalable, governed reporting across the enterprise.
• Partner with business stakeholders across the organization to translate needs into data-driven solutions while upholding data quality, security, and governance standards.
• Act as a trusted advisor to executive leadership, providing strategic recommendations that drive competitive
• Performs other duties as assigned
• Complies with all policies and standards

• Bachelor's Degree in computer science, analytics, engineering, or related field Required
• Master's Degree in computer science, analytics, engineering, business administration, or related field Preferred

• 10+ years hands‑on experience in data engineering, data science, business intelligence, or analytics Required
• 7-9 years in a data leadership role, which involved creating and implementing a comprehensive data strategy and building and managing data engineering and/or BI organizations Required

• Ability to set strategic vision and drive multi-year data and AI roadmaps aligned with business goals and enterprise strategy. (High proficiency)
• Proven ability to reimagine self-service analytics and enterprise data asset management to leverage emerging technology and best‑in‑class patterns. (High proficiency)
• Ability to provide strategic guidance and build cohesiveness among data engineers, data architects, and data analysts. (High proficiency)
• Deep knowledge and experience with advanced SQL concepts. (High proficiency)
• Hands‑on experience with cloud data warehouses (e.g., Snowflake, Databricks,…