Design, develop, and optimize scalable data pipelines using Databricks (PySpark, Delta Lake, Unity Catalog, Lakeflow) and AWS (S3, Glue, Lambda, Step Functions, Redshift)
Lead data ingestion, transformation, and modeling initiatives for enterprise data platforms.
Define and implement robust data models supporting analytics, reporting, and AI/ML use cases.
Gather and translate complex business requirements into scalable technical solutions.
Establish data quality, monitoring, testing, and operational best practices across data platforms.
Mentor engineers, drive architecture standards, and lead end-to-end solution delivery.
Support strategic initiatives including AI readiness, data unification, metadata management, and enterprise integration programs.

Required Skills & Experience
8+ years of experience in Data Engineering, Data Architecture, or large-scale distributed data systems.




Expert knowledge of AWS Data Services and Databricks/Spark ecosystem.
Robust expertise in data modeling (Dimensional, Canonical, Data Vault, Domain-Driven).
Advanced SQL and Python development skills with ETL/ELT experience.
Experience with CI/CD, GitHub, DevOps practices, automated testing, and production deployments.
Proven ability to work independently and lead solutions in ambiguous business settings.

Preferred Qualifications
Experience in Asset Management, Wealth Management, or Financial Services.
Knowledge of data quality frameworks, metadata management, dbt, semantic layers, or data mesh concepts.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience)

📌 Aws Databricks Data Engineer Toronto
🏢 Envision Technology Solutions
📍 Toronto