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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, modern 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.
- 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
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