Staff Data Engineer

Das ist der Job

Our mission is to empower the world to discover cannabis by providing choice, access, and innovation.

Darum lohnt es sich

Through modern, scalable infrastructure, our mission is to empower the world to discover cannabis by providing choice, access, and innovation. You will ensure our modern data stack remains highly performant, reliable, and accessible for downstream users.

Observability, Monitoring & Alerting Set up monitoring and alerting frameworks around both ingestion routines and dbt builds, ensuring that all issues are surfaced clearly to the data team.

Compensation & Benefits Unlimited PTO and paid holidays Base Salary starting at 145,000 - 170,000 Medical/Dental/Vision offered to all full‑time employees 401(k) plan with a match.

Nabis is an Equal Opportunity Employer Nabis is seeking to create a diverse work environment because all teams are stronger with different perspectives and life experiences.

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. 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.

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‑effective compute usage. Data Transformation Use dbt to transform raw data within the data warehouse into structured, production‑ready schemas. Write templated SQL and Jinja code to enforce macro‑driven, modular, and DRY development practices.

Perform rigorous data quality checks by implementing native dbt tests. Manage dbt deployments and CI/CD workflows to ensure smooth, zero‑downtime production updates. Track pipeline health metrics to measure and report on overall data freshness and platform reliability.

Self‑Service Application & Tools Development Build data applications that interact with the data warehouse to empower decentralized self‑service analytics. Build internal tooling and libraries to facilitate analytics and ML work.

Qualifications Pipeline Expertise: Proven production experience building, scaling, and maintaining robust ingestion pipelines using APIs, CDC (Change Data Capture), and orchestrators. dbt Mastery: Advanced proficiency in dbt, including production deployments, package management, and writing custom Jinja macros.

Data Warehousing: Strong hands‑on experience manipulating and managing data within cloud data systems (e.g. Snowflake, BigQuery, Databricks). Software Development: Strong proficiency in Python or similar back‑end languages to build operational applications and custom pipeline tooling.

Data Ops: Experience setting up alerting infrastructure and working with version control (Git) and CI/CD pipelines. Nice‑to‑Have Qualifications Experimentation Platforms: Experience integrating or managing infrastructure for A/B testing and statistical computation.

ML Infrastructure: Background supporting ML pipelines, feature stores, or observability tools. Semantic Views: Production experience configuring and maintaining semantic views or unified metrics layers (e.g., Snowflake Semantic Views).

We strongly encourage women, people of color, LGBTQIA individuals, people with disabilities, members of ethnic minorities, foreign‑born residents, older members of society, and others from minority groups and diverse backgrounds to apply.

We do not discriminate on the basis of race, gender, religion, color, national origin, sexual orientation, age, marital status, veteran status, or disability status.

All employees and contractors of Nabis are responsible for maintaining a work culture free from discrimination and harassment by treating others with kindness and respect. #J-18808-Ljbffr