Growth Platform, Marketing Engineer

<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Faire</strong></span></p> <p>Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.</p> <p>We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.</p></div><h3>Role Overview</h3> <p>Faire’s marketing org runs largely on systems marketing does not currently build. Audiences and attributes, campaign automation, landing pages, attribution, translation, paid feeds, monitoring and alerting: today most of it gets assembled request by request, or waits on a partner engineering pod carrying its own roadmap. The Marketing Engineer builds those systems inside marketing, on Faire’s production stack, and enables marketers to then run solo.</p> <p>This is an engineering role sitting inside marketing. You will write production code across Faire’s codebase, own services end to end, take part in on-call, and put agents into marketing workflows with the evals and monitoring that make them trustworthy at scale. You will be measured on whether the marketing org gained a capability it did not have before.</p> <p>The surface you own can span various disciplines: Paid media tooling and feed automation, the landing page framework, lifecycle and campaign systems, the marketing data layer, internal platform tooling, and the consumption layer for Faire’s recommendation and propensity models inside marketing surfaces are all in scope. You will help define how we work with our broader EPDD partner teams.</p> <h3>&nbsp;</h3> <h3>What you'll build first</h3> <p>Before you join, these problems will probably have moved, you should be comfortable with that. Below are two current problems we’re looking at solving short term:</p> <p><strong>Marketing-led landing pages.</strong> Our current marketing led landing pages workflow is slower than it should be for the blog, newsroom and content teams, and for landing page tests in lifecycle and paid we’re often spending more time talking/prioritizing experiments than building experiences for them. You will own this surface end to end, and you will own the recommendation on where it goes next: you might decide between vendor vs. in-house solution, or build a self-serve, agentic landing page service. The outcome is simple, better, faster landing pages, more self serve, and happy partner teams.</p> <p><strong>Self-serve audiences and attributes.</strong> Our partner teams currently go through us for Audience syncing with paid platforms and marketing led lifecycle. We think this should be a self service capability available to our marketers: whether that's a vendor or in-house, how the data contract gets enforced in code, and what the self-serve layer looks like are all open questions this person will answer to ensure we reach the right person at the right time with the right message.</p> <p>Other projects on our radar: a learning loop for generative assets in ad campaigns, improving infra behind core intelligence feeds powering our paid campaigns, real-time recommendation management across lifecycle and paid, and more.</p> <h3>How we work</h3> <ul> <li>Own services end to end. Design, build, review, deploy, monitor.</li> <li>Ship in Faire's production codebase, to Faire's engineering standards, with code review by engineers.</li> <li>Sit with lifecycle, paid, brand and product marketing rather than behind a ticket queue. Find the pattern behind the requests, then decide what to build so they stop arriving.</li> <li>Put agents into the patterned majority of the queue, with an eval harness, monitoring and a defined failure mode before anything reaches a customer.</li> <li>Turn ambiguous asks into a scoped, prioritized plan, and communicate tradeoffs and progress to non-technical stakeholders.</li> </ul> <h3>Qualifications</h3> <h4>Required</h4> <ul> <li>4+ years shipping and owning production software, in or adjacent to a marketing, growth or GTM organization.</li> <