Engineering Manager, Software, Catalog Enrichment

Responsibilities

• Lead, mentor, and grow a team of engineers building the platform, pipelines, and interfaces that power catalog enrichment and ML access to catalog data.
• Define and execute the technical roadmap, balancing platform investments with reliability, observability, and developer experience.
• Build and operate an AI-native enrichment platform combining LLMs, classical ML, rules, workflow orchestration, and human review.
• Drive autonomous pipeline construction, where users express goals in plain language and the system assembles, evaluates, and optimizes workflows.
• Own the ML-facing catalog data layer, including canonical product metadata and policy controls for internal model inputs and customer-facing use cases.
• Partner with ML, search, ads, commerce, and retailer teams to streamline data flows and remove integration friction.
• Establish human-readable controls for source prioritization, rights management, licensing, and compliance.
• Improve developer and operator productivity through AI-assisted integration testing, investigation tooling, and on-call automation.
• Set and meet SLAs for core attributes and model usage.
• Contribute to architecture discussions, design reviews, and code reviews.

Requirements

• 7+ years of software engineering experience, including 2+ years managing engineers as a people leader.
• Experience leading teams building and operating production data or ML platforms with batch and streaming components.
• Strong foundation in distributed systems, data pipelines, and workflow orchestration.
• Demonstrated build, buy, and partner decision-making.
• Experience owning data quality, coverage, or compliance commitments.
• Excellent written and verbal communication skills.

Nice-to-haves

• Experience with catalog, commerce, or product data platforms at large scale.
• Production experience with workflow orchestration systems such as Temporal, Airflow, or Flink.
• Experience shipping applied AI/ML features and evaluating trade-offs among LLMs, classical ML, deterministic rules, and human review.
• Cross-functional partnership experience with ML, product, and operations teams.
• Familiarity with data governance, rights management, and licensing controls.
• Experience building pipeline builders, low-code/no-code tooling, or internal platforms for non-engineers.
• Developer productivity tooling experience, including AI-assisted testing and investigation workflows.
• Experience operating in fast-growing environments where the platform and team are evolving.

Compensation and Benefits

• Base pay for Canadian candidates: CA$196,000–CA$207,000 CAD.
• Compensation depends on permanent work location, experience, and required skills.
• Eligible for a new hire equity grant and annual refresh grants.
• Benefits are provided according to the employee’s work location.