Senior Data Engineer

About this role

Responsibilities

• Unlike our core local marketplace—where customers pick up "Surprise Bags" in-store—our Parcels business delivers ambient, shelf-stable surplus food directly from major FMCG manufacturers and wholesalers to the customer's door.
• In this role, you won't just sit in a central data team; you will be embedded directly alongside Product Managers, Tech Leads, and Software Engineers in the Parcels product domain. You will act as the dedicated data engineer for Parcels, designing the data pipelines and warehouse architecture needed to support direct-to-consumer logistics, inventory management, and manufacturer partner analytics—all while remaining connected to our overarching central data infrastructure.
• Tech Stack
• Our data stack consists of Airflow, Python, dlt, AWS (Redshift, Lambda, Kinesis, S3, Glue), dbt, Metaflow, and Terraform managed via GitOps principles.
• Product-Embedded Data Architecture: Design, build, and maintain real-time and batch data pipelines tailored specifically to logistics, inventory management, and operational warehouse processing.
• Operational Maturity: Drive the shift from manual processes to scalable, automated data solutions to support high-growth supply chain features.
• Cross-Functional Collaboration: Partner closely with Product Managers and Tech Leads within the Parcels domain to translate operational use cases into clear, robust technical requirements.
• Central Infrastructure Ownership: Serve as the primary link between the Parcels team and the central Data Platform team, balancing embedded tickets with overarching platform standards.
• Data Quality & SLAs: Ensure high data availability, lower latency (e.g., tracking how long parcels take to reach consumers), and strong data governance across warehouse and logistics workflows.
• Key Requirements
• Logistics & Ops Mindset: Experience working with operational, inventory, or supply-chain data systems (e.g., warehouse latency, logistics tracking, or order fulfillment).
• Technical Mastery: Strong proficiency in Python and SQL, with deep knowledge of modern data warehouse internals (preferably AWS Redshift).
• Pipeline & Infrastructure: Demonstrated experience with Airflow (or similar orchestrators), dbt, and Infrastructure-as-Code (Terraform).
• Data Ops & CI/CD: Experience applying DataOps, GitOps, and CI/CD tools (GitHub Actions, Docker) to streaming or batch data applications.
• Strong Communication & Stakeholder Management: Ability to push back on requirements, simplify complex operational needs, and thrive in a fast-paced, agile environment.