Data Engineer, Staff
Qualcomm · San Diego, CA
- On site
- Full-time
- $165,000 / year
- San Diego, CA
Job highlights
- Build and operate scalable data platforms.
- Develop reusable data engineering frameworks.
- Implement AI for automation and optimization.
- Ensure data reliability and governance.
- Lead data architecture and mentor engineers.
About the role
Staff Data Engineer
Qualcomm is seeking a Staff Data Engineer to design, build, and operate a modern, scalable data platform with Databricks Lakehouse as a core foundation. This role focuses on creating reusable data frameworks, shared platform components, and standardized pipelines to enable efficient data product delivery. Your work will support analytics, reporting, and advanced use cases like AI and machine learning, emphasizing reliability, governance, developer productivity, and intelligent automation.
This is a hands-on position with significant ownership in data engineering, framework development, AI-driven automation, platform reliability, security, and cost management. You will also contribute to architectural decisions and data standards.
What You’ll Do
Data Engineering, Frameworks & AI‑Driven Automation
- Design, build, and maintain scalable batch and streaming data pipelines.
- Develop reusable data engineering frameworks, libraries, and templates for ingestion, transformation, validation, and publishing.
- Establish standardized patterns for data modeling, transformations, and pipeline orchestration.
- Implement end-to-end data workflows from raw ingestion to curated analytical datasets.
- Leverage AI-based techniques to automate and optimize data engineering workflows (e.g., intelligent schema inference, automated data quality checks, pipeline failure detection).
- Ensure data quality, reliability, and performance across pipelines and shared frameworks.
- Support downstream consumers such as analytics, reporting, and AI/ML teams.
Reliability, Operations & Intelligent Automation
- Define and monitor SLIs/SLOs for data pipelines, frameworks, and platform availability.
- Participate in incident response, on-call rotations, and post-incident reviews.
- Apply AI-assisted monitoring and alerting to proactively detect issues.
- Implement security, compliance, and data governance controls.
- Drive performance tuning and cost optimization, including automated recommendations.
Collaboration & Technical Leadership
- Partner with analytics, application, and platform teams to understand data needs.
- Drive adoption of standardized data frameworks and automation patterns.
- Contribute to data architecture decisions and platform standards.
- Mentor junior engineers and provide technical guidance.
Qualifications
Data Engineering, Frameworks & System Design
- 8+ years of experience building and operating data platforms or distributed data systems.
- Proven experience designing and building reusable data engineering frameworks, libraries, or platform components.
- Strong experience designing scalable, reliable data pipelines using standardized patterns.
- Solid understanding of data modeling, storage formats, schema evolution, and query performance.
- Experience implementing automation in data pipelines, including rule-based or AI-assisted approaches.
- Ability to reason about architectural trade-offs across scalability, cost, reliability, and security.
Cloud & Data Platform Experience
- Strong hands-on experience with AWS (IAM, networking, multi-account setups).
- Proven experience with Databricks Lakehouse, including Delta Lake and Unity Catalog.
- Strong proficiency in Python for framework development, data processing, and automation.
- Experience building data platforms supporting multiple consumers and automated workflows.
Security & Communication
- Understanding of cloud security best practices and data governance.
- Experience working in regulated or compliance-driven environments.
- Strong communication skills and ability to drive adoption of shared frameworks.
Nice-to-Have
- Experience building AI-assisted or intelligent automation for data quality, observability, cost, or performance.
- Experience building internal data platforms or enablement frameworks.
- Experience supporting AI/ML teams as platform consumers.
- Experience with data observability and monitoring tools.
- Experience with enterprise ingestion tools (e.g., Fivetran, HVR).
- Experience with data lineage or metadata management.
- Familiarity with secret management tools (Vault or similar).
- Experience optimizing Databricks performance and cost.
- Experience working with globally distributed teams.
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 skills/competency
- Data Engineering
- Databricks Lakehouse
- Python
- AWS
- Data Pipelines
- AI-driven Automation
- Data Governance
- SQL
- Framework Development
- Scalability
Skills & topics
- Data Engineer
- Staff Data Engineer
- Data Platform
- Databricks
- AWS
- Python
- AI Automation
- Data Pipelines
- SQL
- Big Data
How to get hired
- Tailor your resume: Highlight your 8+ years in data platforms, Databricks, Python, and AI automation experience.
- Showcase technical skills: Emphasize AWS, Delta Lake, Unity Catalog, SQL/NoSQL, and algorithm knowledge.
- Quantify achievements: Use metrics to demonstrate impact in building scalable, reliable data pipelines.
- Prepare for technical questions: Be ready to discuss data architecture, trade-offs, and automation strategies.
- Demonstrate leadership: Articulate your experience in mentoring and driving adoption of best practices.
Technical preparation
Master Databricks Lakehouse (Delta, Unity Catalog).,Strengthen Python and SQL skills for data engineering.,Review AWS services for data platforms.,Practice designing scalable data pipelines.
Behavioral questions
Describe a complex data framework you built.,How do you handle data quality issues?,How would you drive adoption of new standards?,Tell me about a time you automated a process.
Frequently asked questions
- What is the primary focus of the Staff Data Engineer role at Qualcomm?
- The Staff Data Engineer role at Qualcomm primarily focuses on designing, building, and operating a modern, scalable data platform using Databricks Lakehouse. You will develop reusable frameworks, standardized pipelines, and leverage AI for automation to support analytics and advanced use cases.
- What are the essential technical skills required for this Staff Data Engineer position?
- Essential technical skills include 8+ years in data platforms/distributed systems, Databricks Lakehouse (Delta Lake, Unity Catalog), strong Python proficiency, AWS experience, SQL/NoSQL databases, and a solid understanding of data structures, algorithms, and data modeling.
- Is this a remote or on-site position for the Staff Data Engineer role at Qualcomm?
- This Staff Data Engineer position requires full-time on-site work in San Diego, CA, five days a week. It is not eligible for remote work or Qualcomm immigration sponsorship.
- What is the expected experience level for a Staff Data Engineer at Qualcomm?
- The role requires a minimum of 5+ years of IT experience with a Bachelor's degree (or 7+ years without) and at least 3 years each in programming, SQL/NoSQL, and data structures/algorithms. However, the 'Qualifications' section specifies 8+ years of experience building and operating data platforms.
- What kind of automation is expected in this Staff Data Engineer role?
- The role emphasizes AI-driven automation for data engineering workflows, including intelligent schema inference, automated data quality checks, anomaly detection, and pipeline failure detection/self-healing. Experience with AI-assisted monitoring and alerting is also highly valued.
- What is the salary range for the Staff Data Engineer at Qualcomm?
- The provided pay range for the Staff Data Engineer position at Qualcomm is $132,000.00 - $198,000.00 annually. Total compensation also includes a discretionary bonus and potential RSU grants.
- What are the key responsibilities for a Staff Data Engineer at Qualcomm regarding collaboration?
- Collaboration is key; you will partner with analytics, application, and platform teams to identify data needs, drive adoption of standardized frameworks and automation, contribute to architectural decisions, and mentor junior engineers.