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Added 54 mins ago
C
Senior Identity Security Engineer
CPKC
Calgary, AB
- Full-time
…and identity integrations using Active Directory, Entra ID/Azure AD, LDAP, cloud platforms, applications, databases, and network…
Added 54 mins ago
GT
Lead Data Engineer / Data Product Lead – Snowflake
Global Technical Talent, an Inc. 5000 Company
Toronto, ON
- $60 to $80 CAD / hour
- Contract
Deep hands-on experience with Azure Databricks or Databricks on a major cloud platform.
Added 1 hour ago
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- $55,000 to $60,000 CAD / year
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Complete outstanding Azure DevOps (ADO) tasks assigned to the Accounting team.
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Senior Databricks Data Platform Engineer
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DevOps Technical Lead / Architect - Azure & Terraform
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Quality Engineer I
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Brand Technical Specialist (Solutions) - Hashicorp
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Added 7 hours ago
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Global Technical Talent, an Inc. 5000 Company

Lead Data Engineer / Data Product Lead – Snowflake

Toronto, ON, Canada

$60 to $80 CAD / hour · Contract

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Job details

Pay: $60 to $80 CAD / hour
Job type: Contract
Apply by: Dec 4, 2026
Posting language: English
Posted: Nov 4, 2026

Original job description

Lead Data Engineer / Data Product Lead – Snowflake

Location: Toronto, ON

Onsite Flexibility: Hybrid

Contract Details

Position Type: Contract
Contract Duration: 6 months
Pay Rate: C$60.00–C$80.00 / Hour (CAD)

Job Summary

This client is seeking an experienced Sr. Data Engineer / Data Product Lead to support the design and delivery of enterprise-scale data products within a complex life sciences environment. This is a hands-on technical leadership role requiring deep expertise in Databricks, PySpark, Spark SQL, Delta Lake, cloud data platforms, and modern data engineering, along with the ability to own data products end to end - from architecture and design through development, deployment, production support, and downstream enablement.

Key Responsibilities

Own one or more enterprise data products across architecture, design, development, testing, deployment, and ongoing enhancement.
Design and build scalable data solutions using Databricks, PySpark, Spark SQL, Delta Lake, and Python.
Lead architecture across data ingestion, transformation, medallion/lakehouse layers, semantic models, data quality, lineage, and downstream consumption.
Partner with Product Owners, architects, business stakeholders, analytics teams, and engineering leads to translate requirements into scalable technical solutions.
Design reusable and modular data models that support evolving reporting and analytical requirements.
Lead technical design and implementation reviews to ensure adherence to enterprise architecture and engineering standards.
Coordinate delivery across onshore/offshore engineering teams and third-party vendors.
Manage technical dependencies, priorities, risks, and delivery across multiple concurrent workstreams.
Establish standards around data quality, observability, lineage, validation, testing, deployment, and production support.
Mentor engineers and establish clear ownership across functional areas.
Explore emerging Databricks capabilities including Mosaic AI, MLflow, Genie, semantic models, and AI-enabled analytics.

Required Skills

Strong data engineering experience with expertise in ETL/ELT, data modeling, lakehouse architecture, and enterprise-scale data products.
Deep hands-on experience with Azure Databricks or Databricks on a major cloud platform.
Advanced experience with PySpark and Spark SQL.
Strong experience with Delta Lake and Delta tables, including scalable transformation and historical-processing patterns.
Experience implementing medallion architecture across raw, curated, and business-ready data layers.
Hands-on experience with Databricks capabilities such as:

Unity Catalog - Auto Loader - Databricks SQL - Jobs and Workflows - Notebooks and Repos - Change Data Feed - Structured Streaming

Experience integrating Databricks with cloud storage and orchestration platforms such as ADLS Gen2, Azure Data Factory, AWS S3, or similar services.
Strong understanding of Spark performance tuning, optimization, partitioning, and cost management.
Experience with CI/CD, Infrastructure as Code, source control, and production deployment patterns.
Proven ability to own complex data products through the full delivery lifecycle.
Strong stakeholder-management and communication skills.
Experience leading and mentoring distributed engineering teams.

Preferred Skills

Experience within pharmaceutical, biotechnology, or life sciences environments.
Knowledge of commercial, patient, payer, market access, CRM, or channel-partner data.
Experience with specialty pharmacy / specialty distributor data, 3PL data, Veeva CRM, Salesforce, marketing data, or patient/access datasets.
Experience with advanced Unity Catalog governance, lineage, role-based access, row-level security, and column masking.
Exposure to Mosaic AI, MLflow, Genie, GenAI, semantic models, or agent-based analytical solutions.
Databricks certification or equivalent demonstrated expertise.

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: 26-18394 Industry: Data & Analytics

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More about this job

Responsibilities

Own enterprise data products end to end, leading their architecture, development, testing, deployment, production support, and ongoing enhancement using Databricks and related technologies. Coordinate stakeholders and distributed engineering teams, establish engineering and data governance standards, manage delivery risks and dependencies, and mentor engineers.

Requirements

Requires extensive hands-on data engineering experience with Databricks, PySpark, Spark SQL, Delta Lake, medallion architecture, cloud data platforms, and enterprise-scale data products. Also requires experience with production delivery practices, data quality and governance, stakeholder communication, and leading distributed engineering teams; life sciences experience and Databricks certification are preferred.

Skills

Databricks
PySpark
Spark SQL
Delta Lake
Python
Data Engineering
ETL/ELT
Data Modeling
Lakehouse Architecture
Unity Catalog
Data Quality
Spark Performance Tuning
CI/CD
Infrastructure as Code
Stakeholder Management
Distributed Team Leadership

Visa sponsorship

Not detected in the job text

Keywords

Databricks
Azure Databricks
PySpark
Spark SQL
Delta Lake
Python
ETL/ELT
Data Modeling
Lakehouse Architecture
Medallion Architecture
Unity Catalog
Auto Loader
Databricks SQL
Jobs and Workflows
Change Data Feed
Structured Streaming
ADLS Gen2
Azure Data Factory
AWS S3
Spark Performance Tuning
CI/CD
Infrastructure as Code
Data Governance
Data Lineage
Mosaic AI
MLflow
Genie
Life Sciences
Veeva CRM
Databricks Certification

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