Senior Data Engineer

Data Engineer Full-Time Permanent position Location - Toronto Hybrid - 1-2 days per month in office Key Responsibilities: - Design, develop, and maintain the data infrastructure components, ensuring they meet the requirements for AI/ML initiatives and analytics workflows across the organization. - Write efficient, scalable, and maintainable Python code. - Create, optimize, and manage data pipelines, APIs, and services that facilitate the collection, processing, transformation, and delivery of data for AI-driven tasks, with guidance from senior engineers on architectural best practices. - Work collaboratively with software engineers to maintain data integrity, reliability, and accessibility within data-driven applications. - Build software features that support feature engineering, model training, and deployment of AI/ML models. - Implement best practices for monitoring, testing, and CI/CD to ensure data pipelines and related systems are reliable, scalable, and maintainable. - Optimize data storage and retrieval solutions (e.g., data warehouses, lakes, NoSQL databases) to support both analytics and AI workloads, with mentorship from senior team members. - Continuously refine your software development skills by applying design patterns, conducting code reviews, and creating thorough documentation during the data platform's development lifecycle. Experience: - 5 years of hands-on software development experience, with a focus on data-driven applications. - Experience building and managing data pipelines, data processing systems, or APIs, even if on a smaller scale. - Solid understanding of cloud-based data platforms, distributed systems, and containerization. - Strong proficiency in Python or a similar language, with a commitment to learning and adopting software development best practices. Technical Expertise: - Proficiency in backend development frameworks such as Django, Flask, Node.js, or similar. - Strong SQL skills with experience working with relational databases. Familiarity with NoSQL databases is a bonus. - Basic understanding of big data technologies like Spark, Hadoop, or Kafka is an advantage. - Exposure to machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch is a plus. - Familiarity with version control systems (e.g., Git) and foundational CI/CD practices.

Location: Toronto, CA

Posted Date: 10/5/2024