We are seeking a skilled Data Engineer to join our Engineering team. In this role, you will design, build, and maintain scalable data pipelines and platforms that support business intelligence, analytics, and AI-powered applications. You will work closely with software engineers, product teams, and business stakeholders to deliver reliable, high-quality data solutions while contributing to the evolution of our enterprise data architecture.

Key Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines using Python.

Build and optimize enterprise data lakes, warehouses, and lakehouse solutions.

Develop data pipelines that support analytics, AI/ML, vector databases, and Retrieval-Augmented Generation (RAG) applications.

Implement data quality, validation, lineage, and governance processes.

Optimize data storage, processing, and cloud infrastructure for performance and cost efficiency.

Collaborate with cross-functional teams to deliver scalable data solutions.

Leverage AI-assisted development tools to improve engineering productivity and code quality.

Qualifications





Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline.

5+ years of experience designing and supporting production data pipelines.

Robust proficiency in Python and SQL.

Experience with contemporary data platforms such as Snowflake, Databricks, or Microsoft Fabric.

Experience with cloud platforms including Azure, AWS, or Google Cloud Platform.

Hands-on experience with workflow orchestration tools such as Apache Airflow or Azure Data Factory.

Solid understanding of data modeling, governance, and data quality best practices.

Preferred Qualifications

Experience with Docker, Kubernetes, and Infrastructure as Code.

Experience building data solutions for AI, machine learning, or Generative AI applications, including vector databases and RAG architectures.

Familiarity with AI-powered software development tools and up-to-date engineering practices.

📌 Data Engineer Toronto
🏢 Agam Capital
📍 Toronto