Senior Staff Data EngineerWe are seeking a Senior Staff Data Engineer to design, build, and operate a modern, scalable data platform with Databricks Lakehouse as a core foundation.In this role, you will focus on building reusable data frameworks, shared platform components, and standardized pipelines that enable teams to deliver data products efficiently and consistently. Your work will support analytics, reporting, and downstream advanced use cases (including AI and machine learning), with a strong emphasis on reliability, governance, developer productivity, and intelligent automation.This is a hands-on role with meaningful ownership across data engineering, framework development, AI-driven automation, platform reliability, security, and cost management, while contributing to architectural decisions and data standards.This role is full-time onsite (5 days per week) and can be based in San Diego, CA or Boulder, CO.This position is not eligible for Qualcomm immigration sponsorship.Minimum Qualifications:7+ years of IT-related work experience with a Bachelor's degree in Computer Engineering, Computer Science, Information Systems or a related field. OR 9+ years of IT-related work experience without a Bachelor's degree.5+ 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.What You'll DoDesign, build, and maintain scalable batch and streaming data pipelinesDevelop reusable data engineering frameworks, libraries, and templates for ingestion, transformation, validation, and publishingEstablish standardized patterns for data modeling, transformations, and pipeline orchestrationImplement end-to-end data workflows from raw ingestion to curated analytical datasetsLeverage AI-based techniques to automate and optimize data engineering workflows, such as:Intelligent schema inference and evolutionAutomated data quality checks and anomaly detectionPipeline failure detection and self-healing mechanismsExperience building AI-assisted or intelligent automation for:Data quality monitoringPipeline observabilityCost or performance optimizationEnsure data quality, reliability, and performance across pipelines and shared frameworksSupport downstream consumers such as analytics, reporting, and AI/ML teamsDefine and monitor SLIs/SLOs for data pipelines, frameworks, and platform availabilityParticipate in incident response, on-call rotations, and post-incident reviewsApply AI-assisted monitoring and alerting to proactively detect performance issues, data drift, and operational anomaliesImplement security, compliance, and data governance controls across shared data assetsDrive performance tuning and cost optimization, including automated recommendations for resource utilization and workload optimizationPartner with analytics, application, and platform teams to understand common data needs and platform gapsDrive adoption of standardized data frameworks, automation patterns, and best practices across teamsContribute to data architecture decisions, platform standards, and design guidelinesMentor junior engineers and provide technical guidance, including best practices for automating data workflowsRequired Qualifications:8+ years of experience building and operating data platforms or distributed data systemsProven experience designing and building reusable data engineering frameworks, libraries, or platform componentsStrong experience designing scalable, reliable data pipelines using standardized patternsSolid understanding of data modeling, storage formats, schema evolution, and query performanceExperience implementing automation in data pipelines, including rule-based or AI-assisted approachesAbility to reason about architectural trade-offs across scalability, cost, reliability, and securityStrong hands-on experience with AWS, including IAM, networking, and multi-account setupsProven experience with Databricks Lakehouse, including:Delta LakeUnity CatalogStrong proficiency in Python for framework development, data processing, and automationExperience building data platforms that support multiple consumers and automated workflowsUnderstanding of cloud security best practices and data governanceExperience working in regulated or compliance-driven environmentsStrong communication skills and the ability to drive adoption of shared frameworks and automation patterns across teamsNice-to-Have:Experience building internal data platforms or enablement frameworksExperience supporting AI/ML teams as platform consumers (without owning models)Experience with data observability and monitoring toolsExperience with enterprise ingestion tools (e.g., Fivetran, HVR)Experience with data lineage or metadata managementFamiliarity with secret management tools (Vault or similar)Experience optimizing Databricks performance and costExperience working with globally distributed teamsPay range and Other Compensation & Benefits: $158,400.00 - $237,600.00