Lead Product Manager - Core Data Platform

BILL•San Jose, CA

About The Position

We are seeking a strategic and experienced Lead Product Manager to drive the development of our Core Data Platform. This critical role involves defining and executing the strategic vision for the platform, with a focus on enabling advanced data ingestion & storage, data processing, data serving, and applied AI capabilities. The ideal candidate will possess a deep understanding of data engineering, machine learning & AI workflows, and data science & analytics workflows, coupled with a passion for delivering scalable and high-performance data solutions

Requirements

5+ years of product management experience serving data platforms and products used by technical audiences, including data engineers, data scientists, and ML/AI engineers
Proven experience in product management, with a strong focus on data platforms, data infrastructure, or related areas, with a demonstrated understanding of the unique data requirements of AI
Deep understanding of data engineering concepts, including ETL, data warehousing, data lakes, streaming data, and real-time data serving, instrumentation and experimentation, with a focus on how these concepts apply to risk, GTM and AI
Experience with modern data technologies and architectures, including cloud-based data platforms (e.g., AWS, GCP, Azure), data pipelines, feature stores, and knowledge graphs, with specific experience in building for AI/ML
Familiarity with AI data use cases and the data infrastructure requirements for supporting them, including experience with large language models (LLMs) and other generative models
Strong analytical and problem-solving skills, with a data-driven approach to decision-making, and the ability to apply these skills to complex AI challenges
Excellent communication and interpersonal skills, with the ability to collaborate across diverse teams both synchronously and asynchronously
A good balance of technical depth and customer empathy to deliver best-in-class data platform solutions for AI-powered applications

Responsibilities

Develop and communicate a comprehensive platform strategy, aligning with business goals, data governance standards, and the needs of diverse data users, including data engineers, ML/AI engineers, data scientists, analysts, and business stakeholders
Collaborate with engineering, data scientists, and analytics teams to build consensus for a customer-centric data vision and strategy. Drive this vision in a fast-paced environment by creating bold product roadmaps and participating in all levels of execution to deliver key results
Define and prioritize data platform capabilities, including data ingestion, real-time data pipelines for agentic systems, knowledge graph solutions for enhanced AI reasoning, enterprise feature stores for model development, and OLAP for analytical insights. Ensure alignment with evolving business requirements and emerging AI trends
Develop strong value-based metrics and goals that guide the iterative learning and delivery model to validate solutions to the most important data platform challenges as they evolve over time
Champion a self-service approach to platform development, conducting user research, and utilizing feedback to continuously improve and innovate data platform features and functionalities for risk, GTM, analytics and reporting use cases
Lead the development and enhancement of data infrastructure optimized for AI workloads, ensuring scalability, reliability, and performance of both batch and real-time systems
Oversee the integration and optimization of data pipelines and workflows, ensuring efficient data ingestion, transformation, and delivery for AI model training, inference, and agentic decision-making
Drive the evolution of our feature store and knowledge graph to enable advanced analytics, machine learning, and the development of sophisticated agentic systems
Drive the evolution of our client & server side instrumentation, and experimentation platform
Collaborate with data science and AI engineering teams to support model deployment, operationalization, and continuous improvement
Ensure the data platform adheres to data governance, observability, and compliance standards, implementing robust data security and access controls