Data Engineer

The Role We’re looking for a Data Engineer to join our Engineering team and help architect, build, and scale the data infrastructure that powers our product. If you're excited about building from the ground up and shaping how education evolves through data, we’d love to meet you! We’re open to adjusting the role level based on your experience and expertise. Responsibilities (You Will) Own ingestion pipelines (NiFi 2.0 → Kafka → Iceberg → BigQuery/Motherduck) end-to-end Design and maintain data models (star, wide-table, or Iceberg-native) to support real-time and batch analytics Build robust CI/CD, testing, and observability for data flows (GitHub Actions, dbt tests, OpenMetadata, Sentry) Optimize storage + query performance; keep infra costs lean Partner with ML, product, and frontend engineers to ship AI-driven features (LLM text-to-SQL, embeddings, feature stores, LLM-driven data modeling) Champion data quality, governance, RBAC/RLS, and privacy best practices (FERPA, GDPR) Requirements (You Have) 3+ yrs building production data pipelines on GCP, AWS, or Azure (GCP preferred) Hands-on with at least two of: Kafka or Pub/Sub, Apache NiFi, Airflow, Mage.AI, Beam, Spark, Flink, dbt, Iceberg/DuckDB/ClickHouse Strong SQL + Python; Java a plus Experience with schema evolution, CDC, and high-throughput streaming Familiarity with IaC (Terraform) and container orchestration (K8s or Cloud Run) Bias for automation, clear documentation, and fast iteration in a startup environment Bonus points if you have: EdTech or SaaS analytics background Knowledge of LLM evaluation, vector stores, or semantic layers MotherDuck or DuckDB experience