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Global Technical Talent, an Inc. 5000 Company

Databricks Developer

Toronto, ON, Canada

Pay: $75 to $95 CAD / hour
Job type: Contract
Apply by: Oct 30, 2026
Experience: 10 years of experience in data engineering, data platforms or related technical roles.
Posting language: English
Posted: Sep 30, 2026

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Original job description

Databricks Developer

Location: Toronto, ON

Onsite Flexibility: Hybrid

Contract Details

Position Type: Contract
Contract Duration: 6 months
Pay Rate: C$75.00–C$95.00 / Hour (CAD)

Job Summary

Our client is seeking an experienced Senior Azure Databricks Data Engineer to design, build and support scalable enterprise data pipelines and modern lakehouse solutions. The successful candidate will bring deep hands-on experience with Azure Databricks, PySpark, Spark SQL, Delta Lake and Azure Data Factory, with a strong understanding of medallion architecture, data governance, performance optimization and production deployment. This is a senior-level engineering role requiring the ability to work across architecture, analytics, platform and delivery teams to build reliable, scalable and well-governed data solutions.

Key Responsibilities

Design, build and support scalable data pipelines using Azure Databricks.
Develop and maintain ETL/ELT solutions using PySpark, Spark SQL and Delta Lake.
Implement and support medallion / lakehouse architecture across raw, curated and business-ready data layers.
Use Azure Data Factory (ADF) to orchestrate Databricks workloads, manage dependencies and schedule pipeline execution.
Design and optimize Delta tables and Spark workloads for performance, scalability and cost efficiency.
Implement data quality, validation, audit, logging, monitoring and operational controls.
Build and maintain production-ready Databricks jobs, notebooks, repositories and deployment processes.
Support source control, CI/CD pipelines and release management for enterprise data platforms.
Integrate Databricks with Azure services including ADLS Gen2, Azure Key Vault and ADF.
Troubleshoot production issues and support ongoing platform stability and performance.
Collaborate with architecture, analytics, platform and business teams to deliver scalable data solutions.
Apply strong governance, access-control and security practices across the Databricks environment.

Required Experience

10 years of experience in data engineering, data platforms or related technical roles.

Nice-to-Have Experience

Experience supporting large-scale enterprise lakehouse environments.
Financial Services and/or Investments domain experience.

Required Skills

Strong hands-on expertise with Azure Databricks.
Advanced experience with Auto Loader.
Advanced experience with Unity Catalog.
Advanced experience with Databricks SQL.
Advanced experience with Change Data Feed.
Advanced experience with Liquid Clustering.
Advanced experience with Databricks CLI.
Advanced experience with Infrastructure as Code.
Strong understanding of Databricks governance and access control, including role-based access control, attribute-based access control, object permissions, row-level security, and column masking.
Strong hands-on experience with PySpark and Spark SQL.
Experience with Delta Lake, Delta tables and medallion architecture.
Experience working with Databricks Jobs, clusters, notebooks, repositories and production deployment patterns.
Strong experience integrating Databricks with ADLS Gen2, Azure Key Vault and Azure Data Factory.
Experience using ADF for orchestration, scheduling, parameterization and monitoring.
Strong understanding of Spark performance tuning, partitioning, optimization and cost management.
Experience with CI/CD, source control and enterprise data platform support.
Strong troubleshooting and production-support capabilities.
Must be able to communicate and engage with technical and non-technical teams.

Preferred Skills

Structured Streaming.
Delta Live Tables.
Databricks Asset Bundles.
MLflow.
Genie / Agent-based Databricks capabilities.
Advanced data governance and lineage.
Advanced Databricks cost optimization.

About the Client

This client is a leading financial services and banking institution operating within the Canadian market, including among the country's top-tier banks. The organization employs thousands of professionals across technology, operations, analytics, and corporate functions, serving customers at significant scale across Canada. Teams include DevOps engineers, business analysts, enterprise program analysts, and senior data engineers who collaborate across cross-functional and regulated business environments to deliver enterprise-grade platforms and solutions.

About GTT

GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation, a Native American-owned company in Alaska. We highly value diverse and inclusive workplaces and support Fortune 500 organizations across banking, financial services, technology, life sciences, biotech, utilities, and retail sectors throughout the U.S. and Canada.

Job Number: 26-15050 Industry: Software Engineering

#gttca

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More about this job

Responsibilities

Design, build, and support scalable Azure Databricks data pipelines and lakehouse solutions using PySpark, Spark SQL, Delta Lake, and Azure Data Factory. Optimize and govern production data platforms, implement quality and operational controls, support deployments and production issues, and collaborate with technical and business teams.

Requirements

Requires 10 years of experience in data engineering, data platforms, or related technical roles, with strong hands-on expertise in Azure Databricks, PySpark, Spark SQL, Delta Lake, and Azure services. Candidates should also have advanced experience with Databricks governance and capabilities, orchestration, performance optimization, CI/CD, production support, and communication across technical and non-technical teams.

Skills

Azure Databricks
PySpark
Spark SQL
Delta Lake
Azure Data Factory
Auto Loader
Unity Catalog
Databricks SQL
Change Data Feed
Liquid Clustering
Databricks CLI
Infrastructure as Code
Data Governance
Spark Performance Tuning
CI/CD
ADLS Gen2

Visa sponsorship

Not detected in the job text

Keywords

Azure Databricks
PySpark
Spark SQL
Delta Lake
Azure Data Factory
Auto Loader
Unity Catalog
Databricks SQL
Change Data Feed
Liquid Clustering
Databricks CLI
Infrastructure as Code
Medallion Architecture
Lakehouse
ADLS Gen2
Azure Key Vault
Data Governance
Role-Based Access Control
Row-Level Security
Column Masking
Spark Performance Tuning
CI/CD
Structured Streaming
Delta Live Tables
Databricks Asset Bundles
MLflow
Data Lineage
Financial Services
Investments

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