Job Description

• Developing telemetry software to connect Junos devices to the cloud
• Fast prototyping and laying the SW foundation for product solutions
• Moving prototype solutions to a production cloud multitenant SaaS solution
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
• Build analytics tools that utilize the data pipeline to provide significant insights into customer acquisition, operational efficiency and other key business performance metrics.
• Work with partners including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics specialists to strive for greater functionality in our data systems.

Qualification and Desired Experiences

• Master in Computer Science, Electrical Engineering, Statistics, Applied Math or equivalent fields with strong mathematical background
• 5+ years experiences building data pipelines for data science-driven solutions
• Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning model
• Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow
• Good team worker with excellent interpersonal skills written, verbal and presentation
• Create and maintain optimal data pipeline architecture,
• Assemble large, sophisticated data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Experience with AWS, S3, Flink, Spark, Kafka, Elastic Search
• Previous work in a start-up environment
• 3+ years experiences building data pipelines for data science-driven solutions
• Master in Computer Science, Electrical Engineering, Statistics, Applied Math or equivalent fields with strong mathematical background
• We are looking for a candidate with 9+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift
• Experience with stream-processing systems: Storm, Spark-Streaming, etc.
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning model
• Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large disconnected datasets.
• Proven understanding of message queuing, stream processing, and highly scalable ‘big data’ data stores.
• Strong project management and interpersonal skills.
• Experience supporting and working with multi-functional teams in a multidimensional environment.