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.

Requirements

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)
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

Requirements

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)

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

Data Engineer