Senior Manager, Software Engineering - Data Platform & AI Enablement

Doubleverify; United States; On-site; USD 130,000 - 160,000; Full time

Job summary

Blumberg Capital Company is seeking a Senior Manager of Software Engineering for Data Foundation & AI Data Access. This role will lead the teams responsible for the core data platform and data ingress. You'll own the connection between foundational data systems and the application layers that provide accessible data.

Proven experience in managing engineering teams and technical leadership across data architecture is essential, as well as strong collaboration with various stakeholders to drive successful platform strategies.

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, and Data Science.

Responsibilities

• Lead engineering teams responsible for data ingress, pipelines, datalake adoption, Data APIs.
• Own execution and technical direction across Rockerbox’s data foundation and customer-facing data access layers.
• Ensure reliable, timely, and scalable client data delivery.

Skills

Managing engineering teams

Data architecture

Technical judgment

People leadership

Cross-functional collaboration

Job description

Senior Manager Software Engineering,

Data Foundation & AI Data Access

Summary

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.