Senior Data Engineer

What you will do:

• Build the data foundations that power HOOPP’s AI ambitions by transforming structured data, documents, text, and other unstructured content into trusted, accessible assets that fuel AI models, search, and retrieval capabilities.
• Take AI and analytics ideas from whiteboard to production with data scientists, AI engineers, and the business. We prototype fast, prove what works, and retire what doesn't.
• Use Snowflake as a core data warehouse and AI platform, applying capabilities such as Cortex AI to support scalable analytics and emerging AI use cases.
• Architect cloud-native pipelines on AWS that move structured and unstructured data at scale.
• Shape our lakehouse on Amazon S3 by using open table formats, thoughtful data models, and metadata that makes the platform genuinely easy for others to build on.
• Set the engineering standards the broader data team builds on, and raise the bar through design reviews, code reviews, and mentorship.
• Bring engineering rigor to data through automated testing, infrastructure-as-code, and CI/CD that make releases boring and reliable.
• Build quality, lineage, and observability into every pipeline from day one, so trust in the data is the default.
• What you will bring:
• 7+ years of data engineering experience, combined with 3+ years building production data solutions on AWS.
• Hands-on depth with the AWS data stack — for example S3, Glue, Lambda, Athena, Step Functions, and EventBridge.
• Hands-on experience with Snowflake, including its AI and generative AI capabilities (such as Cortex AI, Snowpark, and vector search).
• Strong SQL and Python, with experience building distributed data processing at scale (Spark preferred).
• Demonstrated experience working with unstructured data (documents, text, images, or audio) in support of AI or machine learning use cases.
• Exposure to AI and generative AI data patterns such as embeddings, vector stores, and retrieval-augmented generation.
• Experience with lakehouse architectures and open table formats (Iceberg, Delta, or Hudi), as well as dimensional modelling for analytics.
• Working knowledge of infrastructure-as-code (Terraform or CloudFormation), Git, and CI/CD for data workloads.
• Ability to navigate ambiguity, ask thoughtful questions, and turn emerging technologies into practical, measurable outcomes.
• Strong communication skills, with the ability to explain technical trade-offs to non-technical partners and mentor other engineers.
• Post-secondary education in computer science, engineering, or a related field; AWS Data Engineer or Solutions Architect certification is an asset

This role is eligible to participate in discretionary incentive plan(s), subject to the terms and conditions of the applicable incentive plan text.