Staff Data Engineer

Nabis is the #1 licensed cannabis wholesale platform in the world, supplying $1B+ worth of cannabis products annually. They are seeking a Staff Data Engineer to serve as the core developer and owner of data pipelines and platform tools, focusing on execution, performance, and maintenance.

Responsibilities:

• 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
• 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 (Don't Repeat Yourself) 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
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds, ensuring that all issues are surfaced clearly to the data team
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability
• 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

Requirements:

• Proven production experience building, scaling, and maintaining robust ingestion pipelines using APIs, CDC (Change Data Capture), and orchestrators
• Advanced proficiency in dbt, including production deployments, package management, and writing custom Jinja macros
• Strong hands-on experience manipulating and managing data within cloud data systems (e.g. Snowflake, BigQuery, Databricks)
• Strong proficiency in Python or similar back-end languages to build operational applications and custom pipeline tooling
• Experience setting up alerting infrastructure and working with version control (Git) and CI/CD pipelines
• Experience integrating or managing infrastructure for A/B testing and statistical computation
• Background supporting ML pipelines, feature stores, or observability tools
• Production experience configuring and maintaining semantic views or unified metrics layers (e.g., Snowflake Semantic Views)