Manager, Data Engineering 沃尔玛数据工程师经理

该职位来源于猎聘 About the Role Walmart Global eCommerce China (GEC China) is on a mission to build the most sophisticated and reliable data foundation for one of the world's most complex e-commerce ecosystems. We are looking for a Senior Manager, Data Engineering — a hands-on technical leader who will architect, build, and operate the end-to-end data layer that powers everything from real-time seller analytics to executive business intelligence, from AI/ML feature stores to consumer personalization engines. This role is for someone who thinks in data models before data pipelines, who has shipped production-grade data platforms at e-commerce scale, and who is obsessed with data quality, latency, lineage, and governance.

You will be the chief architect of our data engineering organization — setting technical standards, mentoring senior engineers, driving adoption of AI-powered automation in data workflows, and partnering with product, analytics, and ML teams to ensure every business decision at Walmart GEC China is grounded in clean, timely, trustworthy data. If you can design a lakehouse schema that a data scientist loves, write a Spark job that a platform engineer respects, debug a CDC pipeline at 3am, and present a data strategy to a VP before lunch — we want to talk to you.

What You Will Own

• Architect the GEC China Data Layer: Design and own the enterprise-wide data architecture — from raw ingestion through curated data lake, semantic data models, BI-ready data marts, and ML feature store — for our China e-commerce platform serving millions of consumers and hundreds of thousands of sellers.
• Lead Data Modeling &
  • Governance: Define canonical data models for all core e-commerce domains: orders, GMV, inventory, seller performance, consumer behavior, product catalog, logistics, and financials. Establish and enforce data contracts, schema versioning, and data quality SLAs across all upstream producers and downstream consumers.

• Build &
  • Scale Data Pipelines: Own end-to-end pipeline engineering — batch ETL/ELT, real-time streaming (Kafka/Flink), CDC from transactional databases, and event-driven ingestion — built to handle peak-day e-commerce traffic (11.11, 6.18) without degradation.

• Drive AI-Powered Data Engineering Automation: Champion the integration of AI/LLM capabilities into the data engineering workflow: automated data quality detection, AI-assisted schema inference, LLM-generated SQL/dbt transformations, intelligent pipeline observability, and self-healing data workflows that reduce manual intervention.
• Full Stack Data Platform Ownership: Deliver beyond pipelines — own the data platform stack including the metadata catalog, data lineage graph, access governance layer, query optimization, cost management, and developer-facing data APIs that enable product engineers and analysts to self-serve safely.
• E-Commerce Domain Leadership: Serve as the authoritative data architect for all e-commerce data domains — translating complex marketplace business logic (seller tiers, GMV attribution, logistics SLA, return rates, promotional mechanics) into rigorous, reusable data models that accurately reflect business reality.
• Team Leadership &
  • Technical Mentorship: Manage and grow a team of senior data engineers and tech leads. Set engineering standards, drive architectural review processes, conduct deep code and design reviews, and create a culture of technical excellence, curiosity, and accountability.

• Cross-Functional Partnership: Partner with data science, ML engineering, product analytics, finance, and business intelligence teams to co-design data products. Represent data engineering in executive planning sessions and ensure the data layer roadmap is synchronized with product and technology strategy.
• Cloud Data Platform Strategy: Own the data engineering strategy on China-local cloud (Alibaba Cloud, Tencent Cloud, Volcengine), including platform selection, cost governance, vendor relationship, security and compliance posture (MLPS Level 3, PIPL, data residency), and hybrid connectivity with Walmart's global data infrastructure.

Required Qualifications

• Data Architecture &
  • System Design

  Data Modeling

• E-Commerce Domain Expertise
• Deep Data Engineering Technology Stack

Full Stack Development Capability

• AI-Powered Data Engineering &
  • Automation

• Technical Leadership &
  • People Management

Preferred Qualifications

• Experience at a top-tier e-commerce or technology company: Alibaba, JD, Pinduoduo, Meituan, ByteDance, or equivalent scale — with direct exposure to data infrastructure serving tens of millions of daily transactions.
• Hands-on experience with cross-border e-commerce data: import/export compliance data, customs duty calculation, cross-border tax (VAT/GST), and currency conversion and FX exposure tracking.
• Familiarity with real-time personalization data infrastructure: user event stream processing for recommendation engines, A/B experiment assignment and result attribution pipelines, and real-time inventory availability serving.
• Prior experience building or operating a data mesh organization: defining data domain ownership, establishing self-serve data platform tooling, and creating cross-domain data SLA frameworks.
• Contribution to open-source data engineering projects (Apache Iceberg, Apache Flink, dbt, DataHub, etc.) or authorship of technical blog posts/conference talks on data architecture topics.
• Master's degree or above in Computer Science, Data Engineering, or a related quantitative discipline.
• Proficiency in Mandarin Chinese (native or near-native) for effective collaboration with local engineering, business, and vendor teams.

Walmart, Inc. is an Equal Opportunity Employer By Choice. We believe we are best equipped to help our associates, customers and the communities we serve live better when we really know them. That means understanding, respecting and valuing unique styles, experiences, identities, ideas and opinions while being welcoming of all people. Walmart doesn’t charge any recruitment or similar fee in the recruitment process, including but not limited to interview, offering, and onboarding.