Data Analytics Engineer - Hybrid

JOB TITLE Data Analytics Engineer – Ribbon Analytics (Full Time)

OPPORTUNITY

Ribbon Communications is looking for a Data
Analytics Engineer to design, architect, and implement data processing and analytics applications for real-world telecommunications use cases using ML, large language models, agentic frameworks, and other innovative technologies. Additionally, the role will also require supporting the design and development of data pipelines, data analytics, and visualization solutions on Ribbon Analytics.

Ribbon Analytics is a big data network analytics and security product that collects, processes, and responds to massive amounts of data collected from the network, leveraging machine learning and other techniques to analyze trends and outliers in the data and take action to mitigate security threats, fraud, and other issues in a customer’s network.

We are seeking a self-driven candidate with a strong work ethic and a focus on creating scalable data analytics and machine learning solutions.

LOCATION

Ottawa, ON, Canada

• Design, architect, and implement data processing and analytics applications using the latest data engineering tools, ML, and AI frameworks.
• Participate in code reviews, design discussions, and technical decision-making.
• Collaborate with architects, data scientists, and software developers to design and develop AI/ML workflows and analytics data pipelines using the latest technologies.
• Optimize data workflows for performance, reliability, and scalability.
• Stay current with industry trends and emerging technologies in data science, ML, and data engineering.
• 2 days a week on our Ottawa office increasing to 3 days in January 2026.

• Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
• A Master’s Degree in Computer Science, Electrical Engineering, or Computer Engineering is preferred.
• 2-4 years of related experience in a similar role.
• Strong understanding of database technologies (SQL and No
  
  SQL).
• Experience with distributed data pipeline and data warehousing frameworks such as Apache Flink, Spark, Kafka, Hadoop, etc.
• Strong programming skills in Python, Java, and SQL as they relate to machine learning, data science, and AI/ML frameworks.
• Ability to quickly pick up new tools and technologies to assist in rapid prototyping.

• Experience with Large Language Models such as Anthropic Claude, Sonet, Llama 2, and other models in a production environment.
• Experience with AI frameworks - Lang Chain, Lang Graph, and Vector Databases.
• Experience with microservices architecture and containerization frameworks - Kubernetes, Docker, and Open Shift.
• Experience with cloud platforms and distributed computing environments for NLP tasks, such as AWS, Google Cloud, or Azure.