Principal Engineer, Aviatrix Systems

• 10+ years of software development in building large-scale Data Analytics solutions
• Proficiency in Golang (preferred) or Python
• Proficiency with common telemetry frameworks (Prometheus, Open Telemetry)
• Proficiency with big data pipeline technologies (g., Logstash, FluentD, Kafka, Spark, Hadoop, Storm, Spark, Flink)
• Domain knowledge in networking: (OSI stack, BGP, IP, TCP, UDP, HTTP, DNS; Linux network stack, SDN, network automation)
• Experience with ML libraries (Scikit-learn, Pytorch, Tensorflow or Keras) is nice to have
• Experience with Cloud Platforms a plus
• BS in Computer Science, Software Engineering, Machine Learning, Mathematics, Statistics, or equivalent

Desirable

• We are seeking a highly skilled and innovative Principal Engineer to join our dynamic Data Pipeline team
• The successful candidate will play a crucial role in developing and maintaining the infrastructure to collect telemetry at scale by building highly performant real-time data pipelines
• Architecture/Design: You will be responsible for designing and architecting the data pipeline infrastructure needed for network analytics including data collection, storage, and processing components to ensure scalability, reliability, and performance of the pipeline
• Collection Data: You will lead and coordinate with other engineers to identify and collect relevant data from network devices, such as routers, switches, and firewalls
• Data Processing: You will lead and develop algorithms, processes to transform and cleanse the raw network data. This may involve parsing, normalizing, aggregating, and enriching the data to make it suitable for analysis
• Integration with Analytics Tools: You will have expertise in integrating the data pipeline with various analytics tools and platforms such as data visualization tools and ML
• Performance Optimization: You will be responsible for optimizing the data pipeline to handle large volumes of network data efficiently, minimizing latency and maximizing throughput. This may involve parallel processing, distributed computing and leveraging cloud
• Quality Control – you will be responsible for implementing data quality checks and validation mechanisms to ensure the accuracy, consistency and completeness of the network data flowing through the pipeline
• Monitoring and Maintenance – you will lead in setting up monitoring tools and techniques to monitor the health and performance of the data pipeline
• Collaboration and Leadership – as a data pipeline engineer, you will work closely with cross–functional teams