Senior Product Manager - Catalog and Store

At Salla, we are building the digital backbone of commerce in the region, for thousands of merchants starting, growing and scaling their businesses. Catalog is the layer underneath everything: storefronts, search, recommendations and every channel a merchant sells through read from the same product data. This role owns both sides of that line, the catalog merchants build and the store their buyers shop. Salla does not build the catalog. Merchants do, and often not even the merchant: a freelancer, operations staff, an agency. The buyer never sees the catalog, only whether they can find the product, trust the page and get what they ordered. Your job is to change what people who do not work for you produce, and turn that data into a store that converts. You lead four product managers across Catalog and Store, and you carry the numbers. Areas of impact: product data input, structure and meaning, AI enrichment, store experience and search, and how the data performs downstream. Responsibilities: - Set the strategy for Catalog and Store as one system, so a new category or way of selling is a configuration, not a rebuild. - Own how product data is created and improved, and define quality by how the data performs downstream, not by how many fields are filled. - Own the buyer's path from search to purchase: search relevance, the product page and availability. - Design for the people who actually enter the data, including those who do not own the store, so the good path is the fastest one. - Put AI to work where it earns trust: decide what we generate, what we suggest and what we never touch, and learn from every correction. - Use AI to take merchants from a blank store to a well-designed, ready-to-sell storefront. - Validate every bet against the outcome it promised: experiment where traffic allows, measure where it does not, and stop what does not work. - Decide how much structure to impose and where merchants stay free, including for external channels with strict requirements. - Lead four product managers, two on Catalog and two on Store: give them real ownership, coach their craft and raise the bar of what the group ships. Requirements - Six or more years in product management, including a self-serve platform where users created their own data and you owned its quality. - A number you moved by changing what users produced at the moment of entry, not by cleaning it up afterwards. - Hands-on ownership of a buyer-facing commerce experience, such as search, discovery or product pages, with conversion you moved and experiments that proved it. - AI in production on structured data, with concrete answers on confidence thresholds, error rates and what you did when it was wrong at scale. - Fluency in how product data is structured: taxonomies, attributes, variants, identifiers, matching, and external channel requirements. - You own outcomes rather than roadmaps, can set a strategy others repeat, and have managed and coached product managers without stepping away from the craft. - Full professional fluency in Arabic and English, regional merchant context preferred, and willing to work on-site in Saudi Arabia.

Six or more years in product management, including work on a self-serve platform where users created their own content or data and you were accountable for the quality of what they produced. A number you owned and moved on that data: completeness that meant something, accuracy, time to publish, adoption of a data tool, or the performance of that data somewhere downstream. Evidence that you changed what users produced at the moment they produced it, rather than cleaning it up afterwards in a pipeline. We will ask which one it was. Platform judgment: you have built something once and made it serve many cases through configuration, and you can explain where you drew the line between what was shared and what was specific, and what that choice cost you. AI in production for structured data: extraction, classification, generation, or matching. You can talk concretely about confidence thresholds, error rates, what you let through automatically, and what you did when it was wrong at scale. Literacy in how product data is actually structured: taxonomies, attribute schemas, variants, identifiers, matching and deduplication, and the strict requirements external selling channels impose. You have coached product managers, formally or not, and you are ready to lead a small team without stepping away from the craft yourself. You own outcomes rather than roadmaps, you move quickly through ambiguity, and you build clarity where others see noise.

Own the catalog strategy end to end: sequence it into quarters, defend it in the room, and hold it accountable to the numbers it promised. Own the roadmap for how product data gets created and improved on Salla, prioritized on whether the data works downstream rather than on how many fields are filled. Design for the people who actually do the work, including the ones who do not own the store, and make the good path the fastest path rather than a longer one enforced by rules. Keep a new category, a configuration change rather than an engineering project, and be able to show the difference in effort between the first one and the next. Put AI to work where it earns trust: decide what we generate outright, what we suggest for approval, and what we never touch, with a stated confidence policy and a loop that learns from every correction a merchant makes. Define what catalog quality means at Salla and instrument it honestly, tied to the surfaces that consume the data rather than to completeness for its own sake. Own how product data performs in external selling channels, where the requirements are strict and the rejections are visible. Decide how much structure we impose and where merchants stay free, then defend that line against pressure from both directions. Lead 2 to 3 product managers: give them real ownership, coach their framing and craft, run a reliable cadence, and raise the quality bar of what the group ships. Work as one system with the teams that depend on this data: search and discovery, storefronts, vendors, marketplace, and data.