Senior Engineer- System Integration

About the role

The Senior Engineer, Systems Integration is a mid-career position of independence and ownership of large investigations and projects. The Senior Engineer will define the scope and tasks for the investigations and may perform the efforts themselves or with one or more engineers assisting. The incumbent must possess strong knowledge of modeling techniques and system performance, and can define resources and time needed to estimate project efforts.

Responsibilities

• Leadership & Scope-Setting
• Act as project lead engineer; may lead a technical team
• Define the scope of a task/project; develop or provide input to schedules and budgets
• Define and control product designs
• Mentor other engineers, including guidance on data ingress, cleaning, and infrastructure maintenance/development
• Provide oversight of dashboards, product support/issues, and customer issues
• Platform & Systems Ownership
• Design, build, and maintain enhancements across our machine learning pipeline orchestration, predictive analytics, and device management platforms to keep telemetry, prediction, and control workflows reliable at scale
• Support and maintain our OTA update management system, including campaign monitoring, firmware management tooling, and OTA process support
• Maintain and enhance monitoring dashboards and customer-support data interfaces so monitoring and support teams have reliable, real-time visibility into device, network and platform health
• Lead troubleshooting, stabilization, and root-cause investigation across platform alerting, data synchronization, machine learning pipeline failures, and outage detection issues
• Support the ML automation and job-orchestration infrastructure that enables repeatable, scalable execution of pipeline, monitoring, and automation workflows
• Infrastructure & Cloud Support
• Infrastructure modernization efforts, including migration of legacy systems and services to modern cloud-based environments
• Implement backup automation, data normalization processes, and archival solutions to ensure data integrity and accessibility across systems
• Support AWS cloud infrastructure, including compute optimization, load balancing, service configuration, and security implementation (e.g., SSL, authentication)
• Contribute to database migration and performance stabilization efforts in partnership with data architecture teams
• Data & ML Collaboration
• Partner with data engineering and analytics teams to support machine learning-driven initiatives, including data pipeline development, query optimization, and deployment support for outage detection models
• Collaborate cross-functionally with engineering, digital engineering, and analytics teams to deliver shared infrastructure solutions
• Documentation & Process
• Initiate and manage tickets in the team's ticketing system (e.g., JIRA)
• Write, review, validate, and audit procedures

Qualifications

• Education
• Bachelor's or Master’s Degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience
• Required Experience & Technical Skills
• 5+ years of related experience
• Advanced foundation in engineering fundamentals
• Experience with Grafana, SQL, NoSQL, Python (Jupyter Notebooks), Elasticsearch, C# or other programming languages
• Experience with Amazon Web Services, S3, SageMaker, Ec2, ECS, Glue, Kibana, and other data platforms
• Experience with statistics, econometrics, machine learning, or other mathematical modeling and simulation techniques
• Experience with data visualization and business intelligence (BI) tools
• Experience with monitoring, alerting, and automation systems in a production environment
• Experience with database administration, performance tuning, and migration projects
• Preferred Qualifications
• Strong electronic product knowledge
• Independently solve problems and implement solutions
• Demonstrated appropriate judgment/decision level of authority
• Demonstrated ability to drive projects to completion
• Experience with IoT device, sensor, or endpoint monitoring at scale
• Familiarity with firmware deployment or OTA (over-the-air) update processes
• Experience managing ML pipelines end-to-end, from training through deployment and production monitoring