Position Overview

As the Head of Data Platform Product, you will lead the strategic evolution of our enterprise data infrastructure into the AGI era. This is not a maintenance role; it is a transformative leadership position. You will challenge traditional data warehousing paradigms and drive AI-native thinking across our mature data ecosystem.

Your mission is to redefine how data assets are managed, processed, and productized, building the end-to-end data pipelines, infrastructure, and operational models required to power large-scale model training and AI-driven applications at scale.

Key Responsibilities：

1. AI-Native Strategy & Paradigms Shift

• Drive the strategic transformation of a mature data platform, shifting the organization from traditional BI/Analytics architectures to an AI-Native data ecosystem.
• Redefine the concept of "data assets" for the AGI era, establishing how unstructured data (text, multimodal, synthetic data) is curated, versioned, and valued as a core corporate asset.
• Challenge existing infrastructure boundaries to build a unified data strategy that seamlessly supports both classical business intelligence and advanced foundation model engineering.

2. End-to-End AI Pipeline & Infrastructure Innovation

• Architect and productize the complete data pipeline required for foundation model training, fine-tuning, and evaluation (including massive-scale ingestion, cleaning, tokenization, filtering, and reinforcement learning feedback loops).
• Own the product lifecycle for next-generation data infrastructure, including high-throughput storage, vector databases, feature stores, and compute-optimized data layouts.
• Revolutionize data operations (DataOps) to support the massive scale, velocity, and strict latency requirements of LLM/VLA/Embodied AI workflows.

3. Data Governance & Security for AGI

• Establish modern data governance, privacy, and compliance frameworks tailored for generative AI, ensuring data lineage, IP protection, and secure data access boundaries during model training and deployment.
• Standardize evaluation and data quality metrics specifically for AI data readiness, ensuring high-fidelity data feeds into downstream training clusters.

4. Transformational Team Leadership

• Lead, inspire, and upskill a sophisticated team of technical product managers, fostering a culture of continuous learning, architectural boldness, and AI-first engineering.
• Partner deeply with AI Labs, Infrastructure, and Core Engineering teams to eliminate friction between data generation and model utilization.

Qualifications & Requirements：

Experience & Leadership

• 8+ years of product management experience, with a heavy emphasis on data platforms, backend infrastructure, or highly technical enterprise systems.
• Proven track record of managing and transforming established, mature product/engineering environments; experience driving organizational change and overcoming technical inertia.
• Demonstrated experience collaborating directly with AI/ML research or infrastructure teams to deliver high-impact data solutions.

Technical Capabilities

• Deep understanding of modern big data stacks (e.g., Spark, Flink, Kafka, Lakehouse architectures) combined with strong literacy in AI infrastructure (e.g., GPU/TPU orchestration friction, vector indexing, distributed storage optimization).
• First-principles thinking regarding data modeling and pipeline architecture; ability to evaluate the ROI of building vs. buying emerging AI-data tooling.
• Fluent in the technical requirements of the model development lifecycle—from raw data pre-processing to embedding generation and production inference.

Soft Skills

• Strong "agent of change" mindset—comfortable questioning established best practices to pave the way for AI-native innovations.
• Exceptional communication and stakeholder management skills; ability to articulate complex technical transformations as clear business value to executive leadership.

Preferred Qualifications

• Advanced degree (MS/PhD) in Computer Science, Data Science, or a closely related quantitative field.
• Direct product experience in high-concurrency, petabyte-scale internet or AI-first environments.
• Hands-on experience or deep familiarity with automated data curation pipelines for large language models (LLMs) or multi-modal models.