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.