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Senior Azure Data Engineer (ETL/Lakehouse)
LinkedIn Recruiter Post
Senior Azure Data Engineer needed for a 9 - month contract in Toronto, ON, to build ELT pipelines and Medallion Lakehouse architectures in the Health Services Cluster.
About the role
- Lead Data Engineer / Snowflake Engineer at Brillio in Montreal focusing on Snowflake and Generative AI data solutions. Responsibilities include architecting data platforms and collaborating with AI teams.
Responsibilities
- Architect, design, and optimize Snowflake data platforms on AWS for high performance and cost efficiency
- Lead end-to-end ELT/ETL pipelines using Snowflake, AWS services, and modern data engineering tools
- Implement advanced Snowflake features (Performance Optimization, Warehousing strategy, Data Sharing, Streams & Tasks, Time Travel, Zero Copy Cloning)
- Design data foundations that support Agentic AI and GenAI workloads, including AI-ready datasets, vectorized data, and metadata-driven pipelines
- Collaborate with AI/ML teams to enable autonomous agents, LLM-driven analytics, and intelligent data orchestration
- Provide technical leadership, code reviews, and mentoring to data engineering teams
- Partner with business and product stakeholders to translate analytics and AI requirements into scalable data solutions
Requirements
- 8–10 years of experience in Big Data Engineering / Analytics
- Expert-level Snowflake experience, including large-scale production deployments
- Strong hands-on experience with AWS (S3, EC2, Lambda, Glue, Redshift/Athena, IAM, CloudWatch, Step Functions)
- Proven experience building cloud-native data architectures on AWS
- Solid programming skills in Python and SQL
- Experience with data modeling for analytics and AI use cases
- Hands-on or applied exposure to Agentic AI, Generative AI, or AI‑driven data platforms
- Experience leading or mentoring engineering teams in enterprise environments
- Highly Desirable
- Experience integrating LLMs, autonomous agents, or AI orchestration frameworks with data platforms
- Exposure to vector databases, embeddings, or AI‑optimized data pipelines
- Experience with dbt, Airflow, Kafka, Spark, or similar tools
- Prior onsite experience in large, complex enterprise data ecosystems
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