Staff Data Engineer / Full‑Stack Data Developer (Databricks / Python)

Job DescriptionCompany:Qualcomm IncorporatedJob Area:Information Technology Group, Information Technology Group > IT Software DeveloperGeneral Summary:The Staff Data Engineer / Full‑Stack Data Developer is a senior, hands‑on individual contributor responsible for designing, building, optimizing, and operating data pipelines, curated data products, and Databricks‑native data applications on a modern cloud Lakehouse platform. This role is critical to enabling enterprise analytics, BI, AI/ML, and data‑driven applications, with deep expertise in Databricks, Python, Spark, and Databricks application development.This position requires strong end‑to‑end ownership of data engineering and data app solutions, production‑grade engineering rigor, and the ability to collaborate across platform, analytics, and application teams.This role requires full-time onsite work in San Diego, CA (5 days per week). Minimum Qualifications: 5+ years of IT-related work experience with a Bachelor's degree in Computer Engineering, Computer Science, Information Systems or a related field. OR 7+ years of IT-related work experience without a Bachelor’s degree. 3+ years of work experience with programming (e.g., Java, Python). 3+ years of work experience with SQL or NoSQL Databases. 3+ years of work experience with Data Structures and algorithms.Key ResponsibilitiesData Engineering & DevelopmentDesign, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and Python to support enterprise analytics, AI, and application use cases.Build and manage curated data layers following Lakehouse Medallion architecture best practices (Bronze / Silver / Gold).Develop reusable, modular data transformation frameworks to accelerate delivery across domains.Databricks Application DevelopmentDesign and develop Databricks‑native data applications, including notebook‑based apps, Databricks dashboards, and interactive data experiences for analytics and business users.Build data APIs, parameterized pipelines, and app‑integrated data services leveraging Databricks and Lakehouse capabilities.Partner with analytics, AI, and application teams to embed data and insights directly into workflows and applications.Ensure Databricks apps meet performance, security, governance, and usability standards.Performance, Scalability & ReliabilityOptimize Apache Spark jobs and Databricks workloads for performance, cost efficiency, scalability, and reliability.Proactively address challenges related to data volume, schema evolution, and compute optimization.Implement robust data quality checks, validations, and anomaly detection within pipelines and apps.Production Support & OperationsOwn and support production data pipelines and Databricks applications, including monitoring, troubleshooting, and root‑cause analysis.Ensure high availability, data correctness, and SLA adherence for business‑critical datasets and apps.Contribute to observability, alerting, and operational automation.Full‑Stack Data EnablementCollabo...