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Job Description & How to Apply Below
Overview We are seeking an experienced and highly skilled Principal Databricks Data Engineer to lead the design, development, and optimization of enterprise-scale data platforms and analytics solutions. The ideal candidate will possess deep expertise in Databricks, Spark-based data engineering, contemporary data lake/data warehouse architectures, and financial risk data domains. This role will drive strategic data initiatives, mentor engineering teams, and collaborate with business stakeholders to deliver scalable, secure, and high-performance data solutions.
Location4 Days Work from Office in Toronto, ON.
Key Responsibilities Architect and implement scalable data engineering solutions using Databricks and Apache Spark.
Design and manage enterprise data lake and data warehouse platforms.
Build robust ETL/ELT pipelines for large-scale structured and unstructured datasets.
Collaborate with Finance & Risk teams to support regulatory, reporting, and analytical requirements.
Optimize Spark workloads for performance, reliability, and cost efficiency.
Lead data modernization initiatives and cloud migration efforts.
Establish data governance, quality, lineage, and security best practices.
Mentor junior and senior engineers on architecture, coding standards, and platform best practices.
Partner with cross-functional teams including Data Science, Analytics, Infrastructure, and Business stakeholders.
Evaluate and implement emerging technologies and frameworks within the up-to-date data ecosystem.
Technical Skills Databricks
Apache Spark (PySpark/Scala/Spark SQL)
Data Engineering
Data Lake and Lakehouse architectures
Enterprise Data Warehousing
Hands‑on experience with cloud platforms such as AWS, Azure, or GCP
Solid understanding of distributed computing and big data processing
Experience building high-volume ETL/ELT pipelines
Proficiency in SQL and programming languages such as Python or Scala
Experience with workflow orchestration tools (Airflow, ADF, etc.)
Knowledge of CI/CD pipelines and Dev Ops practices for data platforms
Familiarity with data governance and security frameworks#J-18808-Ljbffr
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