Senior Solutions Architect – Enterprise Data & AI Strategy

Databricks; Charlotte (NC); On-site; USD 219,000 - 301,000; Full time

Job summary

Databricks in Charlotte, NC is seeking a Sr. Solutions Architect to lead the organization's most complex customer engagements, drive consumption strategy, and serve as the definitive technical authority for key accounts.

You will own executive‑level relationships, mentor junior SAs, orchestrate cross‑functional teams, and design scalable lakehouse architectures that enable real‑time data, ML workloads, and enterprise migrations, ultimately expanding platform adoption.

Qualifications

• 8+ years in solutions architecture or senior technical roles.
• Expert-level coding proficiency in Python, SQL, and Spark/PySpark.
• Deep enterprise data architecture and cloud-native platforms.
• Declared technical specialization with executive-level impact.
• Proven track record of driving consumption growth in large accounts.
• Bachelor's or Master's degree in CS/Engineering (or equivalent).

Responsibilities

• Drive technical and consumption strategy for complex engagements.
• Own senior-level technical relationships and act as trusted advisor.
• Lead architecture engagements across multi-workload platforms and migrations.
• Mentor junior SAs and drive cross-functional collaboration.
• Contribute to field engineering content and customer workshops.
• Develop competitive strategies and influence executive stakeholders.

Skills

Solutions architecture

Executive presence

Strategic thinking

Cross-functional leadership

Customer relationship management

Education

Bachelor's or Master's in Computer Science or Engineering

Tools

Python

SQL

Spark/PySpark

Job description

Databricks in Charlotte, NC is seeking a Sr. Solutions Architect to lead the organization's most complex customer engagements, drive consumption strategy, and serve as the definitive technical authority for key accounts.

You will own executive‑level relationships, mentor junior SAs, orchestrate cross‑functional teams, and design scalable lakehouse architectures that enable real‑time data, ML workloads, and enterprise migrations, ultimately expanding platform adoption.