Staff Data Engineer

Role Overview

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.

A defining piece of this role is owning the data architecture behind the DFM AI + IQE integration with our partner ecosystem (Solid Edge, NX, Designcenter, and Teamcenter), building the pipelines, contracts, and observability that move quotes, parts, manufacturability signals, and pricing between systems in real time.

Responsibilities

• 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. Own bidirectional pipelines, joint data models for parts/BOMs/quotes, low-latency signal paths, governance, lineage, and audit posture.
• Build for scale: Architect and optimize reliable batch and streaming data pipelines, data models, and platforms for complex, high-volume data.
• Own the full lifecycle: Take end-to-end accountability for data engineering work from acquisition and transformation 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 team, including contract-testing and schema evolution.
• Solve ambiguous problems: Navigate cross-domain technical challenges and deliver solutions that meet business and technical objectives.
• Develop multi-quarter roadmaps: Translate strategic priorities into concrete technical plans and timelines.
• Collaborate broadly: Partner with engineers, product managers, data scientists, and external partner engineering teams.
• Mentor and elevate: Guide other engineers through design reviews, code reviews, and mentorship.
• Evaluate and adopt: Evaluate emerging tools, platforms, and architectural patterns in data engineering.

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 large-scale systems ownership.
• Deep expertise with cloud data warehouses (Snowflake strongly preferred), including optimization and performance tuning.
• Expert-level SQL and strong Python proficiency.
• Hands-on experience building and optimizing pipelines using modern tooling (dbt, Airbyte, Airflow, or similar).
• Experience with queueing, batch and stream processing (e.g., Kafka, Spark, Kinesis), and scalable data stores (e.g., Apache Iceberg).
• Experience writing database-heavy services or APIs designed for testability and maintainability.
• Deep understanding of CI/CD principles, automated testing, contract testing, and schema-evolution discipline.
• Strong grasp of the AWS data ecosystem and cloud-native infrastructure.
• Excellent communication skills across technical and non-technical stakeholders, including external partners.

Preferred Qualifications

• Enterprise/partner integration experience with PLM, ERP, or large enterprise SaaS systems (Teamcenter, BMIDE, Active Workspace APIs 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 (IaC) such as Terraform or CloudFormation.
• Experience with event-driven architecture, CDC pipelines, and low-latency operational data flows.
• Experience in manufacturing, supply chain, or marketplace environments.