Data Platform Engineer

• Design and operate ingestion pipelines processing high-volume data from global supply chain and procurement systems.
• Define, evolve, and manage data schemas and catalogues-from raw staging to high-quality analytics and feature stores.
• Build end-to-end monitoring and observability for your pipelines: owning data quality, latency, completeness, and lineage at every stage.
• Champion secure, governed data practices
• Collaborate closely with AI, Platform, and Product teams, provisioning data sets, feature tables, and contracts for analytics and machine learning at scale.

• You have experience at tech and product-driven companies, with a big focus on data quality, DataOps and data management at scale
• You have experience developing data warehouses/lakehouses
• You have used a variety of tools across the data stack and can select and implement the right ones for our use case
• You are keen to both design and implement data solutions end-to-end
• You are comfortable managing your own CI/CD and Integrations

Backed by global technology visionaries like Sequoia Capital, Magentic brings together world-class AI engineering (formers from OpenAI, Meta, and AWS) with procurement expertise (formers from McKinsey & Company and ABInBev). Our mission is to make global manufacturing supply chains robust to an ever-changing world. We're pushing the boundaries of AI with next-generation agentic systems that can manage entire workflows. We're focusing on the $3tn market of procurement. As our first Data Engineer you will work closely with AI engineers, product teams, and domain experts to transform raw supply chain data into structured, reliable, and actionable insights that drive decision-making across our platform, while ensuring that we build a platform capable of serving hundreds of Fortune 500 companies simultaneously. This is an opportunity to work on deeply technical challenges with real-world impact at scale - you'll be creating the data foundation for autonomous agents to reason, plan, and act in complex, high-stakes environments.