Staff Data Engineer

Bachelor's degree in a STEM field (or equivalent experience) plus at least 5 years of experience in a data engineering related role, with demonstrated ownership of complex, large-scale data systems.

Deep expertise with cloud data warehouses – Snowflake strongly preferred – including optimization, best practices, and performance tuning.

Expert-level SQL and strong Python proficiency; comfort picking up additional languages as needed.

Hands-on experience building and optimizing data pipelines, architectures, and data sets using modern tooling (dbt, Airbyte, Airflow, or similar).

Demonstrated experience planning and implementing enterprise data architecture across multiple systems and domains, including integrations that cross organizational or partner boundaries.

Working knowledge of queueing, batch and stream processing (e.g., Kafka, Spark, Kinesis), and highly scalable data stores (e.g., Apache Iceberg).

Lead with technical depth – Design and drive the implementation of enterprise-scale data architecture and engineering solutions that span multiple systems and domains.

Own the partner integration data plane – Architect and build the data layer of Xometry's embedded DFM AI + IQE integration with partner Teamcenter and Designcenter.

Own the bidirectional pipelines, the joint data model for parts / BOMs / quotes / manufacturability signals, the low-latency signal path that delivers DFM and pricing feedback back into the designer's environment, and the governance, lineage, and audit posture required for a public-marketplace partner integration.

Build for scale – Architect and optimize reliable batch and streaming data pipelines, data models, and platforms that handle Xometry's complex, high-volume data, including the real-time and event-driven flows that the partner integration depends on.

Own the full lifecycle – Take end-to-end accountability for data engineering work from acquisition and transformation through to delivery, observability, and ongoing performance.

Set the standard – Define and enforce best practices for data modeling, CI/CD, testing, and code quality across the data engineering function, including the contract-testing and schema-evolution discipline required when data crosses a partner boundary.