Senior Manager, Software Engineering - Data Platform & AI Enablement

<h2>Senior Manager Software Engineering,</h2> <h2>Data Foundation &amp; AI Data Access</h2> <p>&nbsp;</p> <p><strong>Summary</strong></p> <p>The Senior Engineering Manager, Data Foundation &amp; Data Access will lead the teams responsible for Rockerbox’s core data platform, data ingress, datalake adoption, APIs, permissions, and customer-facing data access patterns.</p> <p>This role owns the connection between foundational data systems and the application/API layers that make that data usable by internal teams, customers, and AI-enabled workflows.</p> <p>&nbsp;</p> <p>&nbsp;</p> <h2>Responsibilities</h2> <ul> <li> <p>Lead engineering teams responsible for data ingress, pipelines, datalake adoption, Data APIs, permissions, and data access interfaces.</p> </li> <li> <p>Own execution and technical direction across Rockerbox’s data foundation and customer-facing data access layers.</p> </li> <li> <p>Ensure reliable, timely, and scalable client data delivery.</p> </li> <li> <p>Align ingestion, aggregation, API access, permissions, and AI-enabled data workflows under clear ownership.</p> </li> <li> <p>Partner with Product, Applications, Integrations, Data Science, Customer Success, and DV stakeholders on platform strategy.</p> </li> <li> <p>Enable internal teams and customers to access Rockerbox data through APIs, CLI tooling, and future agentic workflows.</p> </li> <li> <p>Improve team efficiency through automation, reduced maintenance burden, and clearer ownership.</p> </li> <li> <p>Manage, develop, and retain engineers through a period of organizational transition.</p> </li> <li> <p>Reduce bottlenecks between Data, Applications, and customer-facing product development.</p> </li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <h2>Required Qualifications</h2> <ul> <li> <p>Experience managing engineering teams responsible for data platforms, pipelines, APIs, or infrastructure.</p> </li> <li> <p>Strong technical judgment across data architecture, data reliability, and application-facing access patterns.</p> </li> <li> <p>Proven ability to lead cross-functional initiatives across Engineering, Product, Data Science, and Customer Success.</p> </li> <li> <p>Track record of delivering platform improvements with measurable business impact.</p> </li> <li> <p>Ability to operate at broader organizational scope beyond a single functional team.</p> </li> <li> <p>Strong people leadership, communication, and execution skills.</p> </li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <h2>Preferred Qualifications</h2> <ul> <li> <p>Experience with datalake or warehouse adoption across multiple teams.</p> </li> <li> <p>Experience building Data APIs, permissions systems, or customer-facing data access layers.</p> </li> <li> <p>Experience with AI-enabled workflows, LLM tooling, or agentic data access patterns.</p> </li> <li> <p>Experience reducing operational load through automation.</p> </li> <li> <p>Familiarity with marketing analytics, MTA, MMM, testing, and customer data platforms.</p> </li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <h2>Success Measures</h2> <ul> <li> <p>Clear ownership across Data, APIs, permissions, and customer-facing access.</p> </li> <li> <p>Reliable and timely client data delivery.</p> </li> <li> <p>Faster execution on AI-enabling Data API initiatives.</p> </li> <li> <p>Broader datalake adoption across internal teams.</p> </li> <li> <p>Reduced dependency bottlenecks between Data and Applications.</p> </li> <li> <p>Improved engineering capacity through automation.</p> </li> <li> <p>Strong retention and development of critical engineering talent.</p> </li> </ul> <p><br>&nbsp;</p>