Databricks Data Engineer (Databricks, AWS, Python, Glue, ETL)
Design and optimize scalable data pipelines and enterprise data platforms using Databricks and AWS. Lead data ingestion, transformation, and modeling initiatives while mentoring engineers and driving architecture standards.
- On-site
- Toronto, ON
- Posted Sep 3, 2026
- Apply by Oct 3, 2026
- 1 position
More jobs you can apply to directly
Similar opportunities posted by employers hiring on Jobs.ca, with no external application form.
Job summary
Role - Databricks Data Engineer (Databricks, AWS, Python, Glue, ETL) Location - Toronto, ON We are seeking a highly experienced Senior Data Engineer with strong expertise in Databricks, AWS, modern data architecture, and data modeling to design and build scalable enterprise data platforms. The role will lead the development of data pipelines, analytics assets, and data foundations supporting platforms such as Advisor360, CRM, ETF, and Mutual Fund data solutions. This is a senior-level, autonomous role requiring strong technical leadership, consultative problem-solving, and architecture expertise. Key Responsibilities • 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 • 12+ years of experience in Data Engineering, Data Architecture, or large-scale distributed data systems. • Expert knowledge of AWS Data Services and Databricks/Spark ecosystem. • Strong 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 environments. 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).
What you’ll do
Design and optimize scalable data pipelines and enterprise data platforms using Databricks and AWS. Lead data ingestion, transformation, and modeling initiatives while mentoring engineers and driving architecture standards.
Requirements
Requires over 12 years of experience in data engineering and architecture with expert knowledge of the AWS and Databricks ecosystems. Proficiency in advanced SQL, Python, and various data modeling methodologies is essential.
Listed skills
- SQLPreferred
- GitHubPreferred
- CI/CDPreferred
- Amazon Web ServicesPreferred
- PythonPreferred
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Databricks
- AWS
- Python
- PySpark
- Delta Lake
- Unity Catalog
- Lakeflow
- AWS Glue
- AWS Lambda
- Step Functions
- Redshift
- SQL
- Data Modeling
- CI/CD
- GitHub
- DevOps
Job areas
- Data & Analytics
- Technology
- Engineering
- Software
- Consulting
Additional details
- Minimum education
- Bachelor’s degree
- Minimum experience
- 12+ years
- Apply by
- Oct 3, 2026
- Posting language
- English
- Working hours
- 40 hours per week
- Seniority
- Mid-Senior level
- Application method
- Direct apply is available