Python)

Qualcomm•San Diego, CA

6d•Onsite

About The Position

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).

Requirements

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.
5+ years of hands‑on data engineering experience, owning production‑grade pipelines and data solutions.
Strong proficiency in Python and Apache Spark (PySpark).
Proven hands‑on experience working with Databricks in production, including Databricks application development.
Strong SQL and data transformation skills.
Experience building and supporting Databricks notebooks, dashboards, and data‑driven applications.
Experience operating and supporting data pipelines and data apps in production environments.
Solid understanding of data quality, reliability, security, and governance.

Nice To Haves

Experience with AWS cloud services (e.g., S3, IAM, EC2, Glue, or equivalent).
Exposure to Unity Catalog, access controls, metadata management, and governed data sharing.
Experience with streaming data pipelines (e.g., Structured Streaming, Kafka).
Familiarity with CI/CD, Git‑based workflows, and Data/Analytics DevOps.
Experience enabling BI, AI/ML, or application‑embedded analytics using Databricks.

Responsibilities

Design, 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.
Design 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.
Optimize 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.
Own 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.
Collaborate with BI, analytics, AI/ML, platform, and application teams to deliver end‑to‑end data solutions.
Enable data consumption across dashboards, reports, Databricks apps, AI models, APIs, and downstream applications.
Translate business and analytical requirements into well‑designed data pipelines and data applications.
Act as a technical leader and mentor, defining best practices for data engineering and Databricks app development.
Participate in architecture reviews, design discussions, and technical roadmaps.
Continuously evaluate and adopt modern Databricks features, GenAI capabilities, and automation patterns to improve developer productivity.