/roles — ROLE_162
Sr/Staff Data Engineer
Growth-stage supply chain software company modernizing EDI data exchange with AI and self-service onboarding
The role
- COMP
- $190K - $225K
- EQUITY
- Competitive equity
- LOCATION
- Canada
- WORKPLACE
- Remote
- EXPERIENCE
- 5+ years
- VISA
- None
- STACK
- AWS, Snowflake, Airflow, Python, Scala, GCP, Azure, Tableau, Power BI, Kafka, Looker
- INDUSTRY
- Software Development, AI, Logistics, B2B
The company
AI-powered EDI platform that helps retailers, brands, and logistics companies connect with trading partners quickly through self-service setup, automated rules, and a pre-connected network.
- STAGE
- growth-stage
- FUNDING
- $80M+ raised
- TEAM
- 100+ people
- FOUNDED
- 2016
- BACKING
- backed by a16z
JD — the work
About the role
You would be the company's second data engineer, sharing ownership of everything data-related alongside the existing data lead. The work covers data products, a data stack that is already modern, and both greenfield and scaling problems spanning infra, data models, and analytics shown to customers. It suits someone who can cover the full data stack, is comfortable with ambiguity, finds the most valuable problems independently, and designs systems other engineers can build upon. The role is remote in Canada.
What you'll do
- Own large areas of data work end to end, covering pipelines, infrastructure, governance, and analytics
- Develop an AI-ready semantic layer, event-based data products, and in-product analytics where data is the product
- Operate the Snowflake, Databricks, and AWS data platform in production, including an on-call rotation
- Start greenfield projects like real-time streaming, governance, and knowledge tooling, while keeping ad hoc asks contained
- Pick the right tools so the stack stays modular and easy to swap as AI reshapes the field
What they're looking for
- 5+ years of data engineering, operating at senior or staff scope
- Breadth across the data stack, from infrastructure and pipelines to modeling and analytics
- Comfort with ambiguity and the judgment to find high-leverage problems on your own
- Systems thinking for designing platforms that other engineers depend on
- Hands-on experience with tools such as Python, Airflow, Kafka, and Snowflake
- Real enthusiasm for messy, real-world data