Technical Product Manager, Data Infrastructure
Confirmed on the employer's own hiring board on Sep 29, 2026. First seen by Alion on Aug 25, 2026. Lightwheel scores B on the Alion truth index.
About the Role
We are looking for a Technical Product Manager, Data Infrastructure to own strategic customer data deliveries and help build Lightwheel’s Data Delivery Platform into scalable, reusable infrastructure.
Responsibilities
Own Data Platform - Define and evolve data schemas, validation, adapters, conversion, cloud transfer, APIs/SDKs, observability, and cost optimization.
Drive Customer Delivery - Translate customer requirements into technical specifications, acceptance criteria, and delivery plans; own programs end-to-end through customer acceptance.
Scale Data Operations - Build systems and processes capable of supporting 100K-1M+ data-hour programs with predictable throughput, reliability, and cost.
Solve Complex Technical Problems - Identify and resolve bottlenecks across compute, storage, networking, data pipelines, APIs, and customer ingestion.
Build Scalable Processes - Standardize delivery workflows, quality gates, versioning, and change management to make customer onboarding repeatable and scalable.
Lead Cross-Functional Execution - Work closely with Engineering, Data Operations, Infrastructure, and customers to drive technical delivery and continuously improve the platform.
Qualifications
3+ years of experience in software engineering, data infrastructure, distributed systems, ML/robotics infrastructure, or customer engineering.
Strong hands-on engineering background with Python, APIs/SDKs, AWS/GCP, Kubernetes, and data pipelines.
Experience building or operating production data platforms or large-scale data pipelines.
Strong problem-solving skills with the ability to diagnose bottlenecks across compute, storage, network, and APIs.
Experience working directly with technical customers and leading cross-functional projects.
Fluent in English and Mandarin.
Preferred Qualifications
Experience in robotics, autonomous driving, embodied AI, or Physical AI.
Experience with large-scale video, sensor, multimodal, or ML training datasets.
Familiarity with LeRobot, MCAP, ROS bags, TB/PB-scale data, or cloud data transfer.
Experience in forward-deployed engineering, enterprise data integration, or building infrastructure functions from the ground up.