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