Principal Databricks Data Engineer

Role 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.

Location: 4 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 modern data ecosystem.

Technical Skills

• Strong expertise in:
• 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.
• Strong 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 DevOps practices for data platforms.
• Familiarity with data governance and security frameworks.