The 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.