Data Engineering Advisor

Lieu du mandat :Montreal (Hybrid – 2 days/week onsite)

Type de poste :Full-time Contract

Durée de la mission :6 months, renewable

Heures de travail :37.5 hours per week

Début :ASAP

Langue requise :Candidates must reside in Canada (Remote work from Canada only)

À propos du poste

We are looking for an experienced Data Engineering Advisor to design, build, and optimize modern cloud-based data solutions.

In this role, you will develop and maintain enterprise data pipelines, integrate data from multiple sources, implement Azure-based data platforms, and contribute to the architecture of scalable analytics solutions. You will work closely with business users to understand analytical requirements while ensuring data quality, performance, and maintainability.

The ideal candidate has strong experience with Azure Databricks, Azure Data Factory, Spark, and Lakehouse architectures, along with solid ETL and SQL expertise.

Exigences et compétences techniques

Exigences principales

Minimum 8 years of relevant experience in Data Engineering

Minimum 4 years of hands-on experience with Azure Databricks

Experience with at least one complete Databricks implementation or migration project

Strong experience with Azure Data Factory

Experience with Apache Spark

Experience with Azure DevOps

• Experience using GitHub
• Strong understanding of Lakehouse architecture
• Strong understanding of the Medallion architecture
• Experience designing and developing data pipelines
• Experience with ETL development and best practices
• Experience integrating, transforming, and consolidating data from multiple systems
• Experience gathering business and analytical requirements
• Experience writing data mapping specifications and documentation
• Strong SQL knowledge

Experience with T-SQL

• Experience with stored procedures and database functions
• Experience working in Agile environments (Scrum or Kanban)
• Strong analytical and problem-solving skills
• Leadership and strategic thinking in data solution design

Nice-to-Have

Python

SAS

• Data security (access models, data protection)
• Solution architecture diagrams

Objectives & Deliverables

• Design and develop enterprise data pipelines
• Build cloud-based data collection and integration solutions
• Consolidate and transform data from multiple systems
• Develop scalable Lakehouse solutions
• Produce data mapping documentation
• Optimize data platforms and pipelines
• Support business analytics initiatives
• Identify opportunities to improve data systems and architectures

Key Responsibilities

Gather business and analytical data requirements

Design and develop cloud-based data platforms

Build and maintain ETL and ELT pipelines

Integrate, cleanse, transform, and consolidate enterprise data

Create data mapping specifications and technical documentation

Develop and optimize Databricks solutions

Implement Azure Data Factory pipelines

Develop Spark-based data processing solutions

Maintain and optimize data platforms

Build proof of concepts and prototypes

Identify opportunities to improve existing data solutions

Collaborate with business and technical teams

Ensure high-quality, scalable, and maintainable data solutions

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