$ cat jobs/staff-data-engineer-nabis-10911650ac5a.json
Staff Data Engineer
About Nabis: Nabis is the #1 licensed cannabis wholesale platform in the world, supplying $1B+ worth of cannabis products annually from hundreds of brands to retailers across California, New York, and Nevada. Our mission is to empower the world to discover cannabis by providing choice, access, and innovation.Through modern, scalable infrastructure, our mission is to empower the world to discover cannabis by providing choice, access, and innovation. We’re at the forefront of this movement, building an innovative, technology-first platform to scale the entire cannabis industry. Backed by Y Combinator and investors, including DoorDash Co-Founder Stanley Tang, NFL Hall of Famer Joe Montana, Gmail creator Paul Buchheit, and Twitch co-founder Justin Kan, Nabis is rapidly expanding across the U.S. with the goal of becoming the largest and most influential cannabis distribution network globally. Location Eligibility: This role is open to candidates based in the following states: CA, CO, FL, GA, IA, ID, IL, KS, MA, MD, ME, MI, MO, NJ, NV, NY, TN, TX, UT, VA, or WA. Applicants residing outside of these states will not be considered at this time. About the Role: We are seeking a Staff Data Engineer to serve as the core developer and owner of our data pipelines and platform tools. In this role you will focus on execution, performance, and maintenance, driving the development of end-to-end data pipelines from ingestion to analytics application delivery. You will ensure our modern data stack remains highly performant, reliable, and accessible for downstream users. Responsibilities: Ingestion & Pipeline Ownership Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse. Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly. Optimize pipeline performance to ensure maximum uptime, high throughput, and cost