We're SweedPos, a product-driven startup building an all-in-one cannabis retail platform. We’re looking for an Analytics Engineer to help us deliver high-quality, scalable, and trusted data models that power both client-facing and internal analytics. Fully remote.
At Sweed, we’re reimagining how cannabis retailers operate. Our enterprise-grade platform combines POS, eCommerce, Marketing, Analytics and Inventory Management into a single, seamless solution—eliminating the need for multiple third-party tools.
We believe in simplicity, efficiency, and innovation. That’s why we build for scalability and performance, making life easier for cannabis retailers while driving real business growth.
At Sweed, we believe in the medicinal potential of cannabis. It has been shown to help with chronic pain, anxiety, depression, and many other conditions. Despite the lingering stigma, we see cannabis as a powerful tool for improving lives.
The industry is evolving rapidly, and we’re here to drive that transformation—making cannabis retail more efficient, accessible, and customer-friendly.
We’ve been on the market for 7 years, continuously growing and refining our product.
Our focus is on earning customer trust, which means constantly improving our delivery processes and rolling out new features. At the same time, we navigate the complex legal landscape of the cannabis industry, ensuring our platform remains compliant and future-proof.
Our total team size is over 200 people:
The development team is distributed globally and organized into cross-functional product teams. These teams typically consist of 8–12 members, including front-end and back-end developers, QA specialists, and analysts.
Each team is led by a Team Lead and a Product Owner, ensuring effective collaboration and clear direction.
Meanwhile, our CEO, account managers, and customer success team are based in the USA, working closely with us to align product development with business and user needs.
Our customers - cannabis retailers - rely on data to make daily business decisions. As an Analytics Engineer, your work will directly power these decisions via clean, performant, and well-governed data models.
You’ll be responsible for transforming raw data into reliable reporting layers — working closely with our Data Architect, engineers, product analysts, and sometimes even clients. You’ll also play a key role in enforcing data quality through testing and validation practices.
Build and maintain analytics data models using dbt - incremental pipelines (merge strategies, hashdiff, SCD Type 1/2) across retail domains (sales, inventory, loyalty, marketing, promotions), with strong emphasis on structure, documentation, and maintainability
Implement data quality tests and validation logic, ensuring accuracy and trust across reporting layers
Own conformed dimensions as shared contracts across downstream consumers
Collaborate with the Data Architect to apply consistent modeling standards and support architecture evolution
Work with internal teams and sometimes clients to clarify requirements and align on metric logic
Translate business needs into robust, reusable data models
Ensure the integrity of client-facing reports, including reliability, freshness, and metric correctness
Contribute to clear documentation, metric definitions, and data contracts
Support the continuous improvement of our modern data stack: dbt, Trino, ClickHouse, Airflow, Cube.dev, Metabase
5+ years of experience in analytics engineering, data engineering, or BI development
Strong SQL skills and hands-on experience with dbt
Solid understanding of data modeling for analytics/reporting, including fact/dimension and SCD patterns design
Experience writing and maintaining data quality tests (e.g. dbt tests, custom SQL assertions, test coverage frameworks)
Experience with modern cloud-based data warehouses (e.g. Snowflake, ClickHouse, Redshift, BigQuery)
Excellent spoken and written English — you’ll communicate with internal teams and sometimes with external clients
Grain fluency - instinct for when a join will fan out, double-count, or drop rows
Reconciliation thinking - can trace a wrong mart number back to its source
Metric definition - translates ambiguous asks into precise, defensible definitions
Ability to clearly explain data logic and metric definitions to non-technical stakeholders
Meticulous approach to documentation, testing, and ownership of data artifacts
Trino / Presto specifically
Semantic layer experience (Cube.dev, dbt Semantic Layer, LookML)
Experience supporting client-facing dashboards or embedded analytics
Multi-tenant data warehouse experience
Proactivity – We love team members who take initiative and provide feedback
Critical thinking – We value problem-solvers who think beyond just writing code
Adaptability – Our industry is evolving fast, and we need people who thrive in change
Salary in USD (B2B contract with the US company)
100% remote – We’re a remote-first company, no offices needed!
Flexible working hours – Core team time: 09:00-15:00 GMT (flexible per team)
20 paid vacation days per year
12 holidays per year
3 sick leave days
Medical insurance after probation
Equipment reimbursement (laptops, monitors, etc.)
Recruiter Call (up to 45 minutes) – Intro & expectations
Hiring Manage Call (up to 45 minutes) - Deep dive into your Data background
Technical Interview (up to 1.5 hours) – SQL, dbt, data modeling, and DQ test logic
Final Interview (up to 1 hour) – Chat with Data Architect and Product stakeholders