Data Engineer

We’re Sweed, a product-driven company building an all-in-one cannabis retail platform. We’re looking for an Analytics / Data Engineer to help us build reliable, scalable, and trusted data models that power both client-facing and internal analytics.

OverviewApplication

Hi there!

We’re Sweed, a product-driven company building an all-in-one cannabis retail platform. We’re looking for an Analytics / Data Engineer to help us build reliable, scalable, and trusted data models that power both client-facing and internal analytics.

About Us

At Sweed, we’re reimagining how cannabis retailers operate. Our enterprise-grade platform combines POS, eCommerce, Marketing, Analytics, and Inventory Management into one seamless solution - helping retailers replace multiple disconnected tools with a unified operating system.

We’ve been on the market for 7+ years and continue to grow, refine our product, and support cannabis retailers in a complex and highly regulated industry.

Why This Role Matters

We’re currently migrating our analytics and reporting ecosystem from several legacy systems to a new modern data platform.

This is not a pure BI or dashboard-building role. The main focus is on building reliable data marts, migrating reporting logic, improving data quality, and helping us mature our analytics engineering practices.

You’ll work closely with our Data Architect, Analytics Engineers, Data Engineers, and Client Reporting team. The role sits between analytics and data engineering: we need someone who can understand business requirements, but also think technically about pipelines, partitioning, query performance, modeling, and reconciliation.

Current Data Context

Our current landscape includes several legacy layers built around ClickHouse and Snowflake. We’re now moving toward a more scalable modern data stack centered around dbt + Trino.

ClickHouse is still heavily involved in the legacy environment, so experience with ClickHouse - especially around migration, optimization, and reconciliation - would be a strong plus.

The environment is still evolving. Some parts of CI/CD, testing, documentation, and data quality processes are already in place, while others are actively being built and improved.

Team Setup

You’ll join the Data Layer team, which currently includes:

• Data Architect / Team Lead

• 3 internal Analytics Engineers

• 1 external contractor

• close collaboration with the DWH / Data Engineering team

• regular interaction with the Client Reporting team as the main internal stakeholder

The DWH team owns ingestion, replication, source systems, and infrastructure. The Data Layer team owns the transformation and analytical layer after the data lands: dbt models, marts, reporting logic, reconciliation, documentation, and data contracts.

What You’ll Do

• 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

What We’re Looking For

• 5+ years of experience in analytics engineering, data engineering, BI development, or similar data-focused roles

• Strong SQL skills

• Hands-on experience with dbt

• Solid understanding of analytical data modeling: facts, dimensions, grains, SCD patterns, data marts

• Understanding of ETL/ELT pipelines and reporting layers

• Experience with query optimization, partitioning, incremental models, and data pipeline reliability

• Experience with data reconciliation and investigating inconsistent metrics

• Ability to work with business requirements and ask strong clarifying questions

• Good written and spoken English

• Strong ownership mindset and ability to work autonomously in a fast-changing environment

• Willingness to learn, adapt, and dive deeper into technical details

Nice to Have

• Trino / Presto experience

• ClickHouse experience

• Airflow experience

• Experience with Metabase, Superset, Cube.dev, Looker, Tableau, or similar BI / semantic layer tools

• Experience with data contracts

• Experience with retail, marketplace, eCommerce, fintech, payments, or transactional data

• Experience in migration projects

• Experience working in distributed / remote teams