Engineering Manager, Catalog

The role

This role leads the Catalog Enrichment engineering group, which develops the platform and data pipelines that generate and maintain product attributes for tens of millions of items across more than 100,000 retailer locations. The position also owns the ML-facing layer that governs how catalog data is used by downstream models and customer-facing products.

What you would do

• Grow and guide a team building enrichment pipelines and ML data interfaces
• Set the technical direction, weighing new platform work against uptime, monitoring, and developer ergonomics
• Build an enrichment system that combines language models, classical ML, rules, orchestration, and manual review in a form non-engineers can operate
• Move toward systems that assemble and tune workflows from high-level natural-language goals
• Govern canonical product metadata and the controls separating internal model inputs from external uses
• Work with ML, search, advertising, commerce, and retailer groups to simplify data movement
• Champion transparent, auditable controls for source ranking, rights, and licensing
• Improve engineer and operator efficiency through AI-assisted testing, diagnostics, and on-call tooling

What they asked for

• Seven or more years of software engineering work, with at least two years leading engineers
• Track record leading teams that run production data or ML systems, covering both batch and real-time processing
• Solid understanding of distributed architecture, data flow design, and job scheduling systems
• Accountability for data accuracy, coverage, or compliance outcomes, backed by concrete processes
• Clear communication skills for explaining technical work to varied audiences

Nice to have

• Background with large-scale product or catalog data systems
• Hands-on use of workflow engines such as Temporal, Airflow, or Flink in production
• Shipping applied AI features and weighing LLMs against classical ML, deterministic rules, and human review
• Record of aligning ML, product, and operations teams around shared outcomes
• Familiarity with data governance and licensing controls across many stakeholders
• Experience building low-code tooling or internal platforms for non-engineers
• Work on developer productivity tools such as AI-assisted testing and investigation