Data Engineer (Azure) (Hybrid - Toronto)

at  Capco

CAPCO – THE FUTURE. NOW.

Capco is a distinctly and positively different place to work. Much more than consultants, we are active participants in the global financial services industry. Our passionate business and technology professionals enjoy a unique environment where they are actively encouraged to apply intellect, innovation, experience and teamwork. We ware dedicated to fully supporting our world class clients as they respond to challenges and opportunities in: Banking & Payments, Capital Markets, Insurance, Wealth & Asset Management and Energy. Experience Capco for yourself at capco.com.

LET’S GET DOWN TO BUSINESS

Capco is looking for talented, innovative and creative people to join our incredible and growing Team focused on our financial services clients. We are looking for experienced talent exceptional domain expertise who can work directly with our clients on mission-critical projects.

REQUIRED QUALIFICATIONS:

• 3+ years of experience as an Azure Developer
• Proficient in Azure Data Factory development, including activities, triggers, and pipelines
• Hands-on experience with Azure Databricks, including creating notebooks, writing Spark code, and managing clusters
• Strong skills in data transformation, data quality, and data modeling
• Adept at programming in languages like Python, Scala, or SQL
  Preferred Qualifications:
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• Familiarity with CI/CD and infrastructure-as-code (Terraform, ARM templates)

• Certifications in Microsoft Azure, Data Engineering, or related domains
  Professional experience is important. But it’s paramount you share our belief in disruptive innovation that puts clients ahead in a tough market. From day one, your key mission will be to perceive new and better ways of doing things to give your clients an advantage.

• Design and develop data integration pipelines using Azure Data Factory
• Implement ETL/ELT processes to ingest, transform, and load data from various sources
• Leverage Azure Databricks for advanced data processing, machine learning, and analytics
• Optimize data pipelines for performance, reliability, and scalability
• Collaborate with data engineers, BI analysts, and business stakeholders
• Troubleshoot and resolve issues in data integration workflows
• Participate in the development of data governance and security frameworks
• Document data lineage, metadata, and pipeline architectures