Lead Grid Solutions Delivery Specialist

Tapestry; Mountain View (CA); On-site; USD 163,000 - 245,000; Full time

Benefits offered by this job

Generous PTO

Flexible hybrid work model

401(k) with employer contribution

Professional development

Job summary

Tapestry is building the digital backbone for the future of the electric grid. We are seeking a leader to deliver our products to utilities, grid operators, and energy market participants globally.

You will manage data ingestion and transformation, ensuring solutions fit customer environments and deliver value. You will work with power systems experts across teams to resolve complex modeling issues, improve data quality, and accelerate time-to-value.

Qualifications

• Strong power systems background focusing on grid data, modeling, or planning software implementation.
• Experience delivering technical projects to external clients.
• Ability to translate complex engineering challenges into actionable delivery plans.

Responsibilities

• Deploy Tapestry products to utilities, grid operators, and energy market participants.
• Train user groups to move from access to adoption.
• Ingest, clean, and transform large-scale utility datasets and grid models.
• Resolve topology errors and powerflow modeling discrepancies with engineering teams.
• Develop playbooks and contribute to the product roadmap based on deployments.
• Lead special projects outside standard deployment.

Skills

Power systems expertise

Data transformation

Customer presence

Grid data modeling

Education

Bachelor's degree in Power Systems or Electrical Engineering

Tools

CIM

PSS/E

CYME

DIGSILENT

Job description

Tapestry is building the digital backbone for the future of the electric grid. We are seeking a leader to deliver our products to utilities, grid operators, and energy market participants globally.

You will manage data ingestion and transformation, ensuring solutions fit customer environments and deliver value. You will work with power systems experts across teams to resolve complex modeling issues, improve data quality, and accelerate time-to-value.