Senior Product Manager, Data Infrastructure

Angi•Denver, CO

9d•$150,000 - $235,000•Remote

About The Position

Angi has been a leader in the home services industry for over 30 years, providing a platform that connects homeowners with skilled professionals. We are a global company with a strong presence and a commitment to creating a positive work environment for our employees. The Data Infrastructure team is responsible for building the foundational platform that supports all other data teams at Angi. This includes managing shared data access, orchestration, data ingestion, and retiring outdated systems. Our goal is to make this platform a self-service product for engineers, analysts, and increasingly for AI agents, ensuring a single source of truth that is trusted by both people and AI. This role is crucial in guiding the product direction of this platform, especially as Angi embraces AI agents in its operations.

Requirements

Five or more years of product management experience on a data or infrastructure platform team, with direct stewardship of technical roadmaps.
Comfortable reading system architecture and pipeline dependencies to make informed sequencing decisions with engineering.
A proven record of successfully completing cross-team migrations and retirements.
Transparency about areas of growth and openness to learning, avoiding the presentation of incomplete information as resolved.
Working knowledge of how AI agents consume data and tools differently from people, and judgment on when a vendor's own agent layer covers a need.
A proactive approach to independently navigating and understanding new technical systems, key contacts, and open questions.

Nice To Haves

Experience weighing compute cost against performance and developer experience, and driving tradeoffs to decisions.
Experience using AI agents in daily work and setting clear norms for safe experimentation with agents and agentic workflows.

Responsibilities

Manage governed platform access, ensuring engineers, analysts, and services have safe, auditable access to shared data, making the secure path the easiest one.
Streamline orchestration and scheduling by consolidating fragmented tools into a single default system and utilizing AI-assisted tooling for faster job migration with less manual effort.
Drive data ingestion and replication by defining self-service ingestion processes for teams bringing their data onto the platform.
Oversee the retirement of legacy systems that are no longer the source of truth, ensuring clear ownership handoffs.
Optimize compute cost efficiency by identifying and reducing costs driven by habit without compromising the developer experience.
Advance compute strategy diversification by moving the platform towards a storage layer compatible with multiple compute engines, allowing each workload to run on its optimal engine.
Build the platform's AI-ready substrate, ensuring agentic workflows are safe by default with governed access and audit trails for agents similar to those for people.
Use AI agents in daily work and set clear norms for safe experimentation with agents and agentic workflows among engineers and peers.
Protect the team's focus and drive rigor without micromanaging, stewarding outcomes rather than tasks.
Build trust across engineering, domain teams, and leadership through empathetic, transparent, and clear communication when addressing complex challenges.
Collaborate closely with other Data Engineering delivery teams (Data Mesh Enablement and Managed Data Products) and the team building Angi's agentic workflows to align on an overall technical strategy.