Manager – AI & Data Platform Engineering

Title: Manager – AI & Data Platform Engineering Location: Santa Clara, California, USA

Role

We are looking for a technically strong, hands-on Manager – AI & Data Platform Engineering within the Enterprise Data & AI Platform Engineering department, based on a Hybrid (3 days in office) basis in Santa Clara, CA, and reporting directly to the Senior Manager – Data Strategy, to lead the evolution of our enterprise data platform into an AI-ready data and knowledge platform. Sitting at the intersection of Data Engineering, AI Platform Engineering, Knowledge Engineering, and Generative AI, you will combine a deep foundation in modern data engineering and cloud platforms with practical experience building RAG systems, knowledge graphs, AI/LLM data pipelines, and production-grade AI infrastructure. This is a hands-on technical leadership role spanning architecture, engineering execution, people leadership, and platform strategy.

What You’ll Do (Role Expectations)

• Lead a team of data and AI engineers across the US and India, owning performance, career development and day-to-day execution across both full-time staff and contractors
• Own the Data Platform (Snowflake, dbt, Matillion) and the in-house pipelines that build the enterprise's knowledge and AI foundations, turning structured and unstructured data into trusted, AI-ready assets such as knowledge graphs and vector stores, accountable for quality, reliability and delivery
• Turn platform strategy and architecture direction into roadmaps, quarterly plans and delivered outcomes, partnering with architects and engineering leadership and shifting focus across data, knowledge and AI work as priorities change
• Run the platform as a product for internal customers such as domain delivery teams, BI, context and semantic teams and AI engineering, defining service levels, onboarding paths and paved roads that make self-service and AI-native development fast and safe
• Build agentic operations that automate the team's operational work, such as pipeline monitoring, failure triage, data quality checks and remediation, and establish a reusable framework and standards that other teams can adopt for their own operations

Who You Are (Success Profile)

• You are comfortable diving deep into architectural discussions, removing roadblocks for team members, and evaluating effort estimates effectively, all while empowering your senior and principal engineers to lead system design.
• You possess a comprehensive understanding of the complete engineering lifecycle from enterprise data ingestion and transformation to knowledge retrieval, context engineering, and production agent deployment.
• You are equally adept at driving architectural strategy and engaging in technical implementation details, making pragmatic decisions to eliminate technical debt.
• You are an inspiring people leader committed to hiring, mentoring, and advancing engineering team members while cultivating a culture of ownership and engineering excellence.
• Versatile across data platforms, knowledge engineering and agentic operations, you’re equally at home in any of the three. You lead a globally distributed team with an owner’s mindset and a bias for action, and you thrive in a fast-moving AI landscape.

What We're Looking For (Minimum Qualifications)

• Practical understanding of Generative AI architectures, particularly RAG and enterprise knowledge systems, with experience designing pipelines for structured and unstructured data
• 10+ years of experience in software engineering, data engineering, data platform engineering, or data architecture
• 3+ years of experience leading engineering teams with demonstrated technical and people leadership
• Strong hands-on expertise in Python and SQL, with a solid foundation in modern data engineering and distributed systems
• Experience designing and operating production-grade data platforms on AWS (or another major cloud platform) using modern tools such as Snowflake, Databricks, or BigQuery, along with dbt
• Strong grounding in data quality, metadata, lineage, governance, and observability, with a proven ability to translate ambiguous problems into scalable engineering solutions

What Will Make You Stand Out (Preferred Qualifications)

• Production experience building RAG or GraphRAG platforms, Knowledge Graphs, and graph databases (ArangoDB, Neo4j, Amazon Neptune), vector databases, embeddings, hybrid retrieval, and re-ranking
• Hands-on experience building infrastructure for AI agents and agentic workflows using frameworks such as LangGraph, LangChain, or LlamaIndex, alongside LLM evaluation, AI observability, and LLMOps
• Deep cloud-native platform engineering expertise across Kubernetes, Terraform, and Ray/Ray Data, with a track record of building internal developer platforms and reusable data and AI platform services

#LI-YC2 #LI-Hybrid