Data Engineer, Senior Staff

Qualcomm

Quick summary

Work type: On-site
Location: San Diego, CA · Boulder, CO
Salary: $158,400–$237,600 / yr
Posted: 25 days ago
Closes: Nov 8, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $185k

This role $198k

$119k most similar roles pay here $250k

This role pays more than 66% of similar roles. Most pay $155,520–$214,500 — the shaded band above. At the midpoint, this role pays about $198k versus about $185k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 558 open roles on FindRole.

Listed pay typically runs $154,000–$231,000 across 401 roles with salary data.

Most-posted roles

View all roles at Qualcomm

At a glance

TL;DR · Data Engineer, Senior Staff

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As a Senior Staff Data Engineer at Qualcomm’s Information Technology Group, you will join a dynamic team to design and build a modern, scalable data platform using Databricks Lakehouse as its foundation. Your daily responsibilities include developing reusable data frameworks, shared components, and standardized pipelines that support analytics, reporting, and AI/ML use cases. You will leverage Python, Java, SQL, and NoSQL databases to ensure reliability, governance, and developer productivity while implementing intelligent automation techniques for data engineering workflows. This hands-on role requires expertise in AWS, Databricks Lakehouse, Delta Lake, Unity Catalog, and experience with cloud security best practices, making it ideal for someone passionate about driving technical leadership and collaboration across teams.

Skills

Databricks AWS Python Delta Lake Unity Catalog CI/CD SQL NoSQL Data Structures AI Machine Learning Kafka Hadoop Spark Terraform Git Jenkins Prometheus Grafana Kubernetes Vault Fivetran HVR

What you'll do

Design and build scalable batch and streaming data pipelines.
Develop reusable data engineering frameworks for ingestion, transformation, and validation.
Implement AI-based techniques to automate and optimize data workflows.
Ensure data quality, reliability, and performance across shared platforms.
Define SLIs/SLOs and apply AI-assisted monitoring for proactive issue detection.

What we're looking for

8+ years of experience building and operating data platforms or distributed data systems.
Proven experience designing and implementing reusable data engineering frameworks and libraries.
Strong hands-on experience with AWS, including IAM, networking, and multi-account setups.
Extensive knowledge of Databricks Lakehouse, Delta Lake, and Unity Catalog.
Solid understanding of data modeling, storage formats, schema evolution, and query performance.
Experience in automating data pipelines using rule-based or AI-assisted approaches.
Understanding of cloud security best practices and experience working in regulated environments.

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