Staff Data Engineer

About

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.

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. Everything you build will matter. A defining piece of this role is owning the data architecture behind one of Xometry’s highest-leverage strategic initiatives: the DFM AI + IQE integration with our partner. You will be the data engineering lead for the digital thread that connects Xometry’s platform to the partner ecosystem — Solid Edge, NX, Designcenter, and Teamcenter — building the pipelines, contracts, and observability that move quotes, parts, manufacturability signals, and pricing between the two systems in real time. The data plane you design is what makes the “science fiction speed” digital thread from ideation to delivery actually work.

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 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.

Requirements

• 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.
• Deep understanding of CI/CD principles and demonstrated experience with automated testing, contract testing, and schema-evolution discipline in data pipelines.
• 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.