Databricks Data Engineer modernizing a Canadian enterprise data platform. Building ETL/ELT pipelines and lakehouse solutions supporting reporting, analytics, and AI initiatives.
Responsibilities
Design, develop, and maintain data pipelines within the Enterprise Data Platform (EDP)
Build and optimize ETL/ELT processes for data ingestion, transformation, and integration
Use an Agentic approach to development and ensure outputs meet development standards
Develop scalable and reusable data solutions with Databricks and cloud-based data technologies
Support migration and modernization from HANA environments to a modern data platform
Collaborate with Data Architects on scalable data models and platform solutions
Develop transformation logic and workflows for business and analytics requirements
Ensure data quality, integrity, consistency, and performance
Troubleshoot data issues, bottlenecks, and performance concerns
Participate in code reviews, testing, deployment, and release activities
Work with business and analytics teams to understand requirements and deliver fit-for-purpose solutions
Contribute to platform best practices, documentation, and continuous improvement
Requirements
At least 3 years of hands-on Databricks development experience
4+ years of overall Data Engineering, ETL, or Data Integration experience
Strong experience building ETL/ELT data pipelines and transformation processes
Proven experience working with large and complex datasets
Experience developing data solutions within cloud-based platforms
Solid understanding of data warehousing, data lake, and lakehouse concepts
Experience working in Agile delivery environments
Strong problem-solving and analytical skills
Excellent communication and collaboration abilities
Experience with Agentic frameworks and AI-assisted coding is a value add
Preferred: experience supporting large data platform initiatives
Preferred: experience in significant data migration efforts
Preferred: Azure Data Services and Databricks Lakehouse architecture experience
Preferred: modern data engineering frameworks and best practices
Preferred: exposure to AI, GenAI, Machine Learning, or advanced analytics initiatives
Preferred: large enterprise or consulting environment experience
Preferred: knowledge of data governance, metadata management, and data quality frameworks
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