Staff Data Engineer

About the Role

Xometry is looking for a Staff Data Engineer to join our Data Platform team. This is a senior individual contributor role with broad technical scope and high organizational impact. You will own data architecture decisions, lead the design of scalable pipelines and platforms, and set the engineering bar for how we build and operate our data systems.

You will partner closely with engineering, product, analytics, and machine learning teams to ensure our data infrastructure enables the business to move fast and make high-quality decisions. A defining piece of this role is owning the data architecture behind the DFM AI + IQE integration with our partner, acting as the data engineering lead for the digital thread connecting Xometry's platform to the partner ecosystem (Solid Edge, NX, Designcenter, and Teamcenter).

How You'll Contribute

• Lead with technical depth: Design and drive the implementation of enterprise-scale data architecture and engineering solutions spanning 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 bidirectional pipelines, joint data models for parts/BOMs/quotes/manufacturability signals, low-latency signal paths, and the required governance, lineage, and audit posture.
• Build for scale: Architect and optimize reliable batch and streaming data pipelines, data models, and platforms handling complex, high-volume data, including real-time event-driven flows.
• Own the full lifecycle: Take end-to-end accountability for data engineering work from acquisition and transformation to delivery, observability, and performance.
• Set the standard: Define and enforce best practices for data modeling, CI/CD, testing, and code quality across data engineering, including contract testing and schema-evolution discipline.
• Solve ambiguous problems: Navigate cross-domain technical challenges and deliver solutions that meet business and technical objectives.
• Develop roadmaps: Translate strategic priorities into concrete technical plans independently.
• Collaborate broadly: Partner with engineers, product managers, data scientists, business stakeholders, and partner engineering teams.
• Mentor and elevate: Guide other engineers through design reviews, code reviews, and technical mentorship.
• Evaluate and adopt: Make informed recommendations on emerging tools, platforms, and architectural patterns.

What You'll Bring

• 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.
• 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 partner integrations.
• Working knowledge of queueing, batch and stream processing (e.g., Kafka, Spark, Kinesis), and highly scalable data stores (e.g., Apache Iceberg).
• Experience writing database-heavy services or APIs and designing for testability and maintainability.
• Deep understanding of CI/CD principles and automated/contract testing in data pipelines.
• Strong grasp of the AWS data ecosystem and cloud-native infrastructure.
• Ability to operate independently on ambiguous assignments and communicate effectively at all levels.
• Experience integrating with PLM, ERP, or large enterprise SaaS systems (e.g., Teamcenter) is a strong plus.
• Familiarity with data visualization tools (e.g., Looker, Streamlit).
• Experience with data governance, data quality frameworks, and observability tooling.
• Exposure to modern lakehouse or data mesh architectural patterns.
• Experience with Infrastructure as Code frameworks (e.g., Terraform, CloudFormation).
• Experience with event-driven architecture, CDC pipelines, and low-latency operational data flows.