About the role
Assess large-scale batch and streaming architectures, lakehouse design, data contracts, orchestration, lineage, and production data quality. Review AI-generated pipelines for correctness, idempotency, schema evolution, recovery behavior, observability, and cost-aware scalability. Identify weak ownership boundaries, silent data-quality failures, brittle dependencies, and governance gaps that would surface in production. Provide practical corrections using SQL, Spark, Kafka, dbt, Airflow, and comparable modern data-platform patterns. Remote, project-based contract work. Scope and hours depend on project matching; the listed hourly range applies to matched work.
What we're looking for
- 7+ years building production data platforms or analytics infrastructure
- Advanced SQL plus expertise in Spark, Kafka, dbt, Airflow, or comparable systems
- Experience designing reliable, observable, governed data products at scale