Find & Apply For Data Warehousing Specialist Jobs in Seattle, Washington

Our client is currently seeking a Design Engineer - Data Specialist for a 12 month + contract. Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements. Seeking a Mechanical Design Data Specialist to support advanced data-driven initiatives in the engineering domain. In this role, you will bridge the gap between traditional mechanical engineering and modern data operations. You will be responsible for reviewing, evaluating, and annotating complex engineering data to ensure high-quality inputs for computational models and automated design systems. This role involves analyzing design lifecycle changes, and developing the protocols that define how engineering data is structured and used.

Key Responsibilities Data Protocol & Guideline Development:

Act as the 'domain expert' by authoring technical requirements and annotation guidelines. You will define the rules for how engineering intent, constraints, and design rationale should be captured for AI training.

Data Review & Curation:

Audit incoming CAD (NX/Teamcenter/ Solidworks/ CATIA, etc) and CAE (Simcenter/ANSYS) data to ensure it accurately represents real-world engineering intent.

Lifecycle Analysis:

Track and document design changes across versions to create 'diff' datasets that explain why a geometry or simulation parameter was modified.

Synthetic Data Generation:

Use CAD/CAE scripting (e.g., NX Open, Python) to generate variations of mechanical parts and simulation runs to augment training sets. Basic Qualifications BS/MS in Mechanical Engineering or Aerospace Engineering. 5+ years of hands-on experience with commercial CAD/PLM tools (Siemens NX and Teamcenter preferred). Strong understanding of the design-to-simulation loop (FEA/CFD,etc). Proficiency in Python for automating design tasks or data manipulation. Preferred Qualifications Familiarity with engineering design ontologies, knowledge representations, or structured reasoning frameworks for design rationale. Familiarity with Cyber-Physical Systems (CPS) and safety assurance modeling. Hands-on experience with structural analysis (linear/nonlinear statics, dynamics, fatigue) or fluid mechanics at a level sufficient to define engineering constraints and evaluate design feasibility. Experience in developing data collection protocols or annotation schemas—translating complex engineering 'tribal knowledge' into structured, machine-readable requirements. Experience in 'Data-Centric AI'—specifically annotating or preparing non-text datasets (3D meses, point clouds, or field data). Knowledge of Graph Neural Networks (GNNs) or geometric deep learning as applied to mesh-based engineering data.