AVP, Data Engineering and AI Innovation

AVP, Data Engineering and AI Innovation at Safelite—an auto glass company with a broader mission to build fulfilling careers and encourage work-life balance. Let us be the best place you’ll ever work.

About the role

Provide executive leadership across Safelite’s data engineering, data solutions, and AI data-agent teams. Set and drive 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 self-service analytics and enterprise data asset management 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.

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.

Responsibilities

• 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 advantage.
• Perform other duties as assigned.
• Comply with all policies and standards.

Requirements

• 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).

Skills

• 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, BigQuery) and relational databases (high proficiency).
• Deep knowledge of Python (high proficiency).
• Experience with Tableau, PowerBI, or an equivalent BI visualization tool, and Excel (high proficiency).
• Excellent collaboration and communication skills to convey complex data insights to non-technical stakeholders and align initiatives with business goals (high proficiency).
• Working knowledge of engineering-led BI semantic layers and script-based transformation tools (e.g., dbt, AWS Glue, Talend) (high proficiency).
• Experience applying AI/ML and agentic or generative AI approaches to automate data delivery, quality, and insight generation (medium proficiency).
• Working knowledge of code management and version control practices (e.g., GitHub) to support collaborative development and reproducibility across teams (medium proficiency).
• Experience with large enterprise legacy tooling (e.g., mainframe, Informatica, Oracle Business Intelligence, Cognos) (medium proficiency).
• Preferred experience with back office ERP systems (e.g., SAP, Oracle Fusion) and an understanding of core supply chain, field, and finance domains (medium proficiency).
• Preferred experience with digital logging and clickstream/event data and the platforms that capture it (e.g., Adobe Analytics, Google Analytics/GA4, Tealium, Segment, Snowplow).
• Familiarity with contact center and CX data sources (e.g., telephony/IVR, Genesys, CSAT/NPS/voice-of-customer).