Senior Software Engineer - Analytics

at  Netskrt Systems Inc

POSITION OVERVIEW

As a Senior Software Engineer, Analytics, you will contribute to building a world-class, large-scale, analytics platform for the large amounts of data we collect at Netskrt and investigate how we can use AI/ML to optimize our production environments based on that data. Ensuring seamless integration, scalability, and optimal performance. You will collaborate closely with cross-functional teams, architect and implement cutting-edge solutions. This position is based in Vancouver, BC.

REQUIRED QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
• Strong proficiency in Python development with a deep understanding of Python-based applications.
• Extensive experience with big data technologies such as Spark, Hadoop, ClickHouse, and Cassandra.
• Solid understanding of data engineering, data quality, data governance, and data security best practices.
• Strong problem-solving skills and the ability to troubleshoot complex issues in a production environment.
• Strong attention to detail and a commitment to delivering high-quality software solutions.
• Strong communication and collaboration skills, with the ability to effectively convey ideas and data related concepts to both technical and non-technical stakeholders.

DESIRED QUALIFICATIONS:

• Experience with caching and CDN (content delivery network) technologies (CloudFront, Limelight/Edgio, Akamai, Fastly, Netflix, Google)
• Experience with cloud platforms and services, such as AWS, Azure, or Google Cloud.
• Database technologies (PostgreSQL, MYSQL, RabbitMQ)
• Network switching and routing technologies
• Experience with tiered storage solutions
• Familiarity with big data technologies, such as Hadoop, Spark, YARN or Elasticsearch.
• Knowledge of cybersecurity principles and best practices.

• Design, develop, and maintain data pipelines and ETL processes that ingest, transform, and store data from various sources, using technologies such as Spark, Hadoop, ClickHouse, and Cassandra.
• Implement and optimize machine learning models and algorithms, to solve various business problems and deliver insights and recommendations.
• Develop and maintain APIs and web services that expose machine learning functionality and results to internal and external users and applications.
• Perform data analysis and exploration, to understand data quality, distribution, and patterns.
• Work closely with data scientists, software engineers, and product managers to understand business requirements, define data and machine learning solutions, and deliver high-quality results.
• Troubleshoot and resolve issues related to the performance and reliability of data pipelines, machine learning models, and applications.
• Stay abreast of industry trends and emerging technologies, providing recommendations for continuous improvement in data engineering and machine learning practices.