Catalog and classify. Register and classify data assets in Collibra, including student data that is sensitive under FERPA.
Design for secure access. Work closely with our Data Platform Engineer (Security & AI Access) so every model works with per-user, per-location, and purpose-based access controls.
Set the standard. Document models and conventions, define how the team models data, and mentor the peers who follow.
What you'll bring
Strong SQL and hands-on dbt experience: ELT and medallion patterns, testing, documentation, and Git-based workflows.
Proven data modeling across dimensional and wide / denormalized designs, with disciplined metric definitions.
Experience building a semantic or metrics layer. LookML / Looker is ideal, and experience with the dbt Semantic Layer, Cube, or Power BI semantic models also counts.
Production experience on BigQuery or another cloud data warehouse (Snowflake, Redshift, Databricks).
Comfort turning loosely scoped problems into production-ready models, and setting patterns others can follow.
Usually 6+ years in data engineering, analytics engineering, or a related field.
Nice to have
Collibra or a similar catalog / governance tool (Alation, Microsoft Purview).
Python.
Experience with AI-assisted engineering tools such as Claude Code.
Experience with education or assessment data, or with FERPA or other regulated data.
Don't tick every box? Apply anyway. We care more about strong modeling fundamentals and curiosity than an exact match to our stack.
Why you'll love it
Build from zero. You'll design the foundation instead of inheriting legacy code.
Real impact. Your work helps educators understand how students are learning.
AI-ready by design. You'll build a semantic layer that people and AI agents use the same way, under the same rules.
Shape a new team. As an early hire, your standards become the group's standards.
#LI-Hybrid
