Senior Data Engineer

LeafLink is the largest unified B2B cannabis platform, providing licensed cannabis businesses a suite of tools to manage their business more effectively, sell or order from their favorite brands and accelerate growth. We are one platform, one solution and we’re defining the way thousands of cannabis brands, distributors, and retailers streamline their operations. With thousands of brands and retailers across 30+ markets in North America, we are setting the industry standard for how cannabis businesses grow together. LeafLink processes more than $5 billion in wholesale cannabis orders annually.

Our team, backed by funding from leading VC's, including Founders Fund, Thrive Capital, Nosara Capital, and Lerer Hippeau is poised to define the cannabis supply chain through technology.  LeafLink was named one of Inc. 5000’s ‘Top 5000 Fastest-Growing Private Companies’; one of Fast Company's 'Top 10 Most Innovative Companies in Enterprise for 2020', joining the ranks of Amazon, Slack, and VMWare; one of Built In NYC's 'Best Places to Work in 2021'; 2024 Fast Company’s Best WorkPlaces for Innovators for Banking, Finance, and Fintech category; 2024 Green Market Report Award for Best Fintech in cannabis - and we're just getting started!

LeafLink is seeking a Staff Data Engineer to help lead the evolution of our data platform and analytics infrastructure. This role will design and scale systems that power analytics, reporting, and operational insights across the organization.

Staff Data Engineers influence how data is modeled, moved, and consumed across the company. This role partners with engineering, finance, product, and operations teams to ensure the data platform remains reliable, scalable, and capable of supporting critical decision-making.

• →Design and lead development of scalable data pipelines using Airflow, dbt, and Python.
• →Guide architecture and evolution of the company data platform.
• →Build integrations across modern data stack tools including Fivetran, Redshift, and Sigma.
• →Develop serverless data services using AWS services such as Lambda.
• →Partner with stakeholders to define reliable data models and analytics datasets.
• →Improve pipeline reliability through testing, monitoring, and observability.
• →Modernize legacy data processes and improve automation across the platform.
• →Mentor engineers and analysts on data modeling and platform best practices.
• →Drive improvements in data governance and documentation.

• 8+ years of experience building production data pipelines and analytics infrastructure.
• Strong SQL skills and experience modeling large datasets.
• Strong Python experience for building pipelines and automation.
• Experience with Airflow, dbt, or similar orchestration and transformation tools.
• Experience with modern data stack tools such as Fivetran, Redshift, or Sigma.
• Experience with AWS and serverless data processing such as Lambda.
• Strong understanding of data warehouse architecture and analytics engineering
• Demonstrated ability to lead technical initiatives across teams.