Qualcomm
Databricks Lead (Staff IT Engineer)
$120K–$179K
San Diego, California · On-site · Staff · Full-time · Posted Sep 17, 2026
- Experience
- 8–12 years of overall IT experience; 4–5 years of hands-on Databricks experience. Minimum qualification: 4+ years of IT-related work experience with a Bachelor's degree, or 7+ years without a Bachelor’s degree. Relevant advanced degrees may substitute for up to two years (Master’s = one year, Doctorate = two years).
- Education
- Bachelor's degree, or 7+ years of IT-related work experience without a Bachelor’s degree; relevant advanced degrees may substitute for up to two years of work experience.
- Visa sponsorship
- Not eligible for Qualcomm immigration sponsorship.
About the job
Leads the design, development, and operation of enterprise Databricks data and AI platforms, including platform modernization, machine learning enablement, AI-assisted engineering, and reliability automation.
What you'll do
- Design and optimize scalable Databricks Lakehouse platforms and reusable engineering frameworks.
- Build machine learning, AI-assisted development, and AIOps capabilities to improve productivity and operational reliability.
- Lead technical reviews, mentor engineers, and establish coding, testing, and deployment standards.
What they ask for
- 4–5 years of hands-on Databricks experience and strong experience implementing enterprise cloud and AI platforms.
- Experience with Databricks, Delta Lake, Unity Catalog, MLflow, Mosaic AI, Python, SQL, Spark, Workflows, REST APIs, GitHub, CI/CD automation, AIOps, observability, and generative AI tools and frameworks.
- Technical mentoring, problem solving, cross-functional collaboration, continuous improvement, and strong communication skills.
- Full-time onsite work in San Diego, CA or Boulder, CO; role is not eligible for Qualcomm immigration sponsorship.
Pay
$119,600.00 - $179,400.00 (base salary)
Tools
Databricks, Delta Lake, Unity Catalog, MLflow, Mosaic AI, Python, SQL, Spark
AI focus
Promotes AI-assisted software engineering and AI-enabled SDLC practices; operationalizes machine learning and MLOps; builds AI-driven operational insights, intelligent automation, and AIOps capabilities.
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