Senior Data Engineer

Job Title: Senior Data Engineer

About the role

Pearson Virtual Schools runs online K-12 programs, including Connections Academy and Pearson Online Academy, for students and families across the country. Everyone who works on those programs depends on data they can trust: educators tracking student progress, the teams improving our curriculum, and more and more, AI tools working alongside them.

We're starting a new Data Engineering group, and this is one of its first two hires. You'll build our governed semantic layer from scratch. It will be the single, trusted definition of our data and metrics, used both by BI tools and by AI agents. You'll begin with two high-impact domains, student assessment and learning efficacy and marketing / commercial, and the modeling standards you set will shape how the group grows.

This is a greenfield role with a lot of autonomy. You'll pick the patterns, write the conventions, and help the engineers who join after you.

What you'll do

• Model the data. Design and build dbt models on BigQuery across raw, conformed, and serving layers for our first two domains.
• Build the semantic layer. Create and maintain LookML explores, views, and measures as the reusable contract that every dashboard, analyst, and AI agent queries.
• Make the numbers trustworthy. Put dbt tests, documentation, and reconciliation against established reporting sources in place, so educators and data scientists can rely on the results.
• 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.