Senior Technical Product Manager, Data & AI Platform

Viasat, Inc.•Carlsbad, CA

4d•Hybrid

About The Position

We are thrilled to announce an exciting opportunity for a Senior Technical Product Manager, Data & AI Platform to join our dynamic team in Carlsbad, CA, San Jose, CA or Denver, CO! This is a hybrid, site-based role that offers the perfect blend of office collaboration and remote flexibility. Employees will work from one of these listed offices at least 3 days (60%) per week within a standard five-day workweek, with the remaining days worked remotely. This highly technical, platform-minded Senior Technical Product Manager will own the unified data and AI cloud platform at the center of our agentic transformation. This platform is bigger than any single agent — it is the data and infrastructure backbone for our next wave of platform expansion, turning trusted, well-governed enterprise data into the raw material every future AI-driven capability will run on. In this role, you will own the roadmap for core platform infrastructure and drive how it’s exposed through interfaces that work for both technical and non-technical users. Your stakeholders span the full platform: internal engineers building advanced programmatic agents, non-technical business users creating and managing agents through low-code/no-code interfaces, and the teams who depend on a consolidated, well-governed data platform underneath it all. Your job is to understand what each group needs — from foundational data infrastructure to the interfaces built on top of it — and translate that into requirements, partnering with engineering on execution, so the platform is safe, trustworthy, and usable across the board.

Requirements

7+ years of Product Management experience, with a proven track record owning data & AI platforms, infrastructure, or other engineering-enablement platforms where internal users are your customers.
Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
A track record of owning Cloud platform capabilities that serve both technical engineering users and non-technical business users equally well – measuring success via development velocity, platform adoption, and system reliability.
Deep knowledge of modern data architecture (emphasis on GCP) and agentic concepts, paired with a strong understanding of how to translate that complexity into capabilities that provide the highest ROI.
Understanding of orchestration layers and how to translate their parameters into configurable, user-friendly controls.
Proven experience building or managing cloud platforms within highly regulated environments (e.g., FedRAMP, SOC2, HIPAA, or DoD Impact Levels). You understand the implications of strict boundary controls and data isolation.
Demonstrated success leading through formal OKRs — setting measurable objectives, driving alignment across engineering and business stakeholders, and tracking outcomes to inform iteration.
Ability to communicate strategy, trade-offs, and value propositions clearly to both technical and non-technical audiences, from engineering teams to business leadership.

Nice To Haves

Background in Computer Science, Data Engineering, or a related field with an emphasis on AI.
Deep, hands-on familiarity with the Google Cloud Platform ecosystem, specifically Gemini Enterprise Agent Platform, IAM architecture, BigQuery, and GCP security controls

Responsibilities

Own the roadmap for building a unified context layer and consolidating data platforms, readying the platform for agentic AI — including high-throughput data ingestion, semantic modeling, and knowledge catalogs so both developer-built and business-built agents ground decisions in trusted corporate data.
Own the roadmap for ingesting new data sources into the governed Cloud data lake, consolidating data from across business domains — such as finance — into a consistent, easy-to-use format for downstream AI and analytics use cases.
Design the platform with the flexibility to adapt to varying compliance postures. Ensure architecture patterns support strict governance frameworks, data sovereignty, and auditability requirements inherent to government and regulated commercial business.
Work directly with internal customers — engineering and business teams — to define and evolve the semantic layer and underlying data model, ensuring it reflects how they reason about and query enterprise data, not just how the data is stored.
Own the product lifecycle for internal self-service portals, CLI tools, SDKs, and templates that empower software engineers to securely stand up and deploy AI agents with minimal friction.
Establish automated system guardrails and data-privacy boundaries so non-technical users cannot accidentally breach compliance or leak sensitive data.
Define requirements for dashboard analytics showing token spend, agent utilization, accuracy, and ROI across departments.