Staff Data Engineer

Responsibilities

• 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.
• Solve ambiguous problems – Navigate complex, cross-domain technical challenges, evaluate variable factors, and deliver solutions that meet both business and technical objectives.
• Develop multi-quarter roadmaps – Translate strategic priorities into concrete technical plans, working independently to determine methods and timelines.
• Collaborate broadly – Partner with engineers, product managers, data scientists, business stakeholders, and partner partner engineering teams to translate requirements into robust technical solutions.
• Mentor and elevate – Guide other engineers through design reviews, code reviews, and technical mentorship, raising the overall capability of the team.
• Evaluate and adopt – Stay current with the data engineering ecosystem and make informed recommendations on tools, platforms, and architectural patterns.

Qualifications

• 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).
• Experience writing database-heavy services or APIs and designing for testability and maintainability.
• Strong grasp of the AWS data ecosystem and cloud-native infrastructure.
• Ability to operate independently on new and ambiguous assignments, determine methods and procedures, and communicate effectively at all levels of the organization — including with external partner engineering teams.
• Enterprise / partner integration experience – Prior work integrating with PLM, ERP, or large enterprise SaaS systems; partner Teamcenter experience (data model, BMIDE, Active Workspace APIs, AWC integrations) or comparable PLM exposure is a strong plus.
• Familiarity with data visualization tools (e.g., Looker, Streamlit).
• Experience with data governance, data quality frameworks, and observability tooling — especially in contexts where data flows across partner or tenant boundaries.
• Exposure to modern lakehouse or data mesh architectural patterns.
• Experience with infrastructure as code (IaC) frameworks (e.g., Terraform, CloudFormation).
• Experience with event-driven architecture, CDC pipelines, and low-latency operational data flows that feed back into a customer-facing UI.
• Experience in manufacturing, supply chain, or marketplace environments is a plus — but curiosity and drive matter more.