Grüns - Senior Backend Engineer

• Build and own backend services and system integrations across core business platforms including Shopify, Skio (subscriptions), Fulfil.io (ERP), marketing tools, and finance/banking systems • Design, build, and maintain reliable data ingestion patterns using APIs, webhooks, and batch workflows with strong error handling and observability • Write and maintain production-grade services primarily in Python (Node/TypeScript where appropriate) • Productionize data products that support analytics, forecasting, and data science initiatives, ensuring they are stable, scalable, and maintainable • Improve platform reliability across GCP, the warehouse, and orchestration layers through monitoring, alerting, logging, and cost-aware architecture decisions • Translate defined cross-functional requirements into robust technical systems, partnering closely with a project manager to deliver clean execution • Collaborate with the Data team to improve dbt models and analytics workflows when backend system improvements unlock better data outcomes • We're looking for someone who: • 5-7+ years of experience in backend engineering, data engineering, or platform engineering, with hands-on ownership of production services and integrations • Strong proficiency in Python and SQL (TypeScript/Node is a plus) • Experience building and operating production APIs and backend services (REST and/or GraphQL), including authentication patterns, background jobs, and third-party integrations • Experience designing and maintaining data ingestion workflows using APIs, webhooks, and batch processes • Hands-on experience with orchestration tools such as Airflow / Composer (or equivalent) • Cloud infrastructure experience (preferably GCP), including CI/CD, logging, monitoring, and observability practices • Strong reliability mindset with experience building systems that are stable, scalable, and well-monitored • Preferred: Familiarity with dbt and supporting analytics workflows when needed • Preferred: Experience with data quality best practices (tests, reconciliation, idempotency, schema evolution) • Preferred: Awareness of privacy and compliance considerations for data pipelines (GDPR/CCPA principles)