VCA AI Knowledge and Data Operations Sr. Manager

Visa•San Francisco, CA

6d•Hybrid

About The Position

The Sr. Manager, Knowledge Base and Data Operations is responsible for building and maintaining the platform’s knowledge base, ensuring seamless data ingestion from internal sources (such as Visa dashboards and enterprise data) and external feeds. This role manages taxonomy, quality control, and retrieval-augmented generation (RAG) pipelines to guarantee agents access accurate, up-to-date information. The Sr. Manager collaborates on data governance and bias mitigation, supporting the integrity and reliability of the platform’s data assets.

Requirements

8 or more years of relevant work experience with a Bachelor Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD

Nice To Haves

9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
Extensive experience in knowledge base management, data operations, and enterprise data environments.
Proven ability to design and implement data ingestion pipelines and taxonomy structures.
Strong background in quality control, RAG pipelines, and data governance.
Demonstrated expertise in bias mitigation and ensuring data integrity.
Excellent analytical, problem-solving, and technical skills.
Outstanding communication and collaboration abilities.
Ability to manage multiple projects concurrently and deliver results in a fast-paced environment.
Deep understanding of digital trends, data management technologies, and enterprise platforms.
Team player with a collaborative approach and commitment to developing talent.

Responsibilities

Knowledge Base Management: Build, maintain, and optimize the platform’s knowledge base to support AI agent operations.
Data Ingestion: Oversee data ingestion processes from internal sources (e.g., Visa dashboards, enterprise data) and external feeds, ensuring completeness and accuracy.
Taxonomy & Organization: Develop and manage taxonomy structures to enable efficient data categorization and retrieval.
Quality Control: Implement quality control measures to ensure data accuracy, consistency, and reliability.
RAG Pipelines: Design and maintain retrieval-augmented generation (RAG) pipelines to provide agents with timely, relevant information.
Data Governance: Collaborate with stakeholders on data governance policies, ensuring compliance and best practices.
Bias Mitigation: Work to identify and mitigate bias in data sources and agent outputs.
Collaboration: Partner with tech, product, and business teams to align data operations with platform goals.
Continuous Improvement: Monitor data operations, gather feedback, and drive ongoing enhancements to knowledge base and data processes.
Documentation: Maintain clear documentation of data ingestion, taxonomy, quality control, and RAG pipeline processes.