Data Engineer — Subscription Commerce for Shopify SaaS (Remote, LATAM)

## About the Role We build subscription infrastructure for modern commerce. Our platform helps B2B and DTC brands scale repeat deliveries and subscription experiences on Shopify. We're a profitable, customer-funded SaaS company working closely with Shopify on a new channel initiative — and we're entering a phase where our data systems are becoming product infrastructure. We're looking for a Data Engineer who thrives in small teams, has strong backend experience, is passionate about delivering scalable data and analytics, and understands that data pipelines are no longer "just ETL." The systems you build will directly influence merchant operations, subscriber experiences, analytics, forecasting, automation, and customer trust. Some of those systems already behave more like APIs than batch jobs. This role is for someone who enjoys thinking about: - schema contracts - latency tradeoffs - event correctness - replay safety - observability - and what happens when messy real-world commerce data collides with product expectations You'll work closely with other backend engineers, product, customer success, and leadership to evolve our data infrastructure into reliable operational systems that scale with the business. ## Join A Growth-Focused Team At An Important Stage Our backend team has been carrying data engineering alongside platform development. As merchant-facing reporting and operational analytics become core product surfaces, we need someone to take full ownership of the data layer — so our platform engineers can focus on APIs and product work. We have a working Azure + DuckDB foundation already in place, recently migrated off Microsoft Fabric for pragmatic operational reasons. Your job is to grow it into the infrastructure powering merchant reporting, subscription analytics, and the AI and forecasting initiatives we're investing in next. ## What You'll Work On Own production-grade data pipelines end-to-end You'll design and operate systems that ingest and process: - Shopify events and webhooks - subscription lifecycle activity - order and fulfillment data - billing and payment events - customer behavior signals - operational and delivery metrics These pipelines support both analytics and product-facing functionality — including merchant-facing reporting surfaces that customers see every day. Define and enforce data contracts As data becomes part of application behavior, consistency matters. You'll establish: - schema evolution strategies - freshness guarantees - replay and backfill safety - idempotent processing patterns - operational monitoring standards - warehouse and API consistency expectations Improve reliability and scalability We're looking for someone who thinks carefully about late-arriving events, duplicate processing, warehouse performance, event ordering, failure recovery, operational visibility, and scaling analytical workloads alongside production systems. You should enjoy identifying where assumptions break under scale — and designing systems that fail transparently instead of silently. ## Our Stack The data layer is the focus of this role: - Azure — central cloud platform for data infrastructure; blob storage, azure functions and durable functions are heavily leveraged to store and consume analytics data - DuckDB — pipeline and analytical query layer - Shopify APIs & webhooks — primary event source - Event-driven architectures and ETL / ELT pipelines Supporting infrastructure you'll interact with but don't need deep expertise in: - AWS — supports adjacent application services - PHP/Laravel-based services — part of the broader application environment Bonus points for experience with subscription commerce, operational analytics, distributed systems, data contracts, stream processing, warehouse optimization, or real-time data systems. ## What We're Looking For You may be a great fit if you: - Have built and maintained modern data infrastructure in production - Understand both batch and event-driven processing tradeoffs - Think beyond "job success/failure" and toward downstream consumer impact - Care deeply about correctness, observability, and operational clarity - Can reason about scaling systems under production load - Enjoy practical engineering more than buzzwords We care more about systems thinking and ownership than checking every box. ## First 90 Days Month 1 — Get oriented in the existing Azure + DuckDB pipeline. Shadow the team carrying data work today. Identify the top 3 reliability or observability gaps you'd address first. Month 2 — Take primary ownership of the pipeline. Ship your first reliability or schema-contract improvement. Establish operational monitoring you trust. Month 3 — Own the data roadmap. Partner with product on upcoming reporting and analytics deliverables. Begin shaping how the data layer supports forecasting and AI initiatives further out on the roadmap. ## What Makes This Role Different This is not a "move CSVs around all day" data engineering role. The systems you work on will increasingly sit close to: - customer-facing experiences - operational automation - subscription lifecycle behavior - merchant reporting and analytics - AI and forecasting initiatives - product decision-making We believe modern data engineering sits at the intersection of backend engineering, systems design, infrastructure reliability, and product thinking. The engineers who thrive here enjoy ambiguity, ownership, and thoughtful tradeoffs. ## About Us We're a profitable, bootstrapped SaaS company headquartered in the United States. We compete against incumbents by focusing on scaling repeat delivery outcomes that are accurate & operationally tighter, and by working closely with our customers and our Shopify partners. Our products help Shopify brands scale subscription commerce and repeat delivery operations. We've spent years building operational infrastructure for recurring deliveries and are now expanding deeper into analytics, automation, and platform intelligence.