Principal Engineer – Device Analytics

• Design, create, and maintain solutions for connected devices and new form factors
• Build clients for device data collection and provide insights from device data
• Build iOS and Android clients to collect device performance insights
• Build required on-premises infrastructure to process device data and generate insights
• Architect secure on-premises data processing and data engineering solutions
• Maintain on-premises hardware configurations compliant with Verizon security requirements
• Architect and lead comprehensive data integration strategies from multiple sources
• Perform performance tuning and diagnose and resolve complex big data and streaming issues
• Develop and deploy high-capacity data processing, database architecture, and network security solutions
• Oversee integration testing and guide quality assurance teams on enterprise initiatives
• Lead requirements gathering, project scoping, system engineering, and validation lifecycle activities
• Track evolving artificial intelligence and big data analytics methodologies and trends
• Assess and integrate external libraries and software frameworks
• Cultivate technical partnerships with Verizon internal groups and external OEMs
• Convey technical information to non-technical audiences and mentor colleagues

• Bachelor’s degree or four or more years of work experience
• Six or more years of relevant experience, demonstrated through work and/or military experience or specialized training
• Experience designing and implementing production data applications using Java, Python, or Scala on big data platforms
• Deep expertise in big data technologies and data optimization techniques
• Experience with streaming technologies, specifically Kafka
• Experience with open-source software platforms, such as the Apache ecosystem and Linux
• Master’s degree or PhD in Computer Science, Computer Engineering, Software Engineering, Data Science, or a related quantitative field (even better)
• Ability to optimize data flow and design efficient, scalable data pipelines, including data lakes and warehouses on containerized clusters
• Strong understanding of distributed data architecture concepts, such as caching, Map Reduce, and stream processing
• Knowledge of data security, governance, and compliance
• Proficiency in Java, Python, Scala, and SQL
• Strong analytical capabilities, creativity, and critical thinking skills
• Effective written and verbal communication and ability to facilitate team collaboration

Demonstrates expertise in designing and implementing scalable data applications using Java, Python, and Scala, with a strong focus on big data technologies and data optimization. Proficient in architecting secure data processing solutions and integrating diverse data sources while ensuring compliance with security standards.

• Big Data Technologies
• Data Pipeline Optimization
• Java Programming
• Kafka Streaming
• Data Security Compliance

• Java
• Python
• Scala
• SQL
• Big Data Platforms
• Data Optimization Techniques
• Data Lakes
• Data Warehouses
• Map Reduce
• Stream Processing

• Analytical Capabilities
• Creativity
• Critical Thinking
• Effective Communication
• Team Collaboration

• Bachelor's Degree
• Master's Degree
• PhD in Computer Science

• Data Integration Strategies
• Data Governance
• Compliance Standards
• Performance Tuning
• Quality Assurance

• Apache Ecosystem
• Linux
• Containerized Clusters
• On-Premises Infrastructure
• Data Engineering Solutions