Lead Azure Data Engineer
New
CanadaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7–10 years
- Required Skills
- PythonSQLMachine LearningMicrosoft Power BI
Requirements
- 7–10 years of experience in data engineering, business intelligence, or related data platform roles.
- Strong expertise with the Microsoft Azure Data Stack, including Azure SQL, Microsoft Fabric, and Power BI.
- Proven experience designing enterprise-scale cloud data platforms, Data Lakehouse architectures, dimensional models, and modern analytics solutions.
- Advanced SQL skills and strong proficiency in Python for data engineering, automation, and ETL/ELT development.
- Experience leading complex analytics or data engineering projects from design through deployment.
- Ability to combine technical leadership with hands-on engineering contributions.
- Strong understanding of data governance, security, privacy, multi-tenant architectures, and performance optimization.
- Excellent communication skills with the ability to explain complex technical concepts to both technical and business stakeholders.
- Experience with DBT, additional BI platforms, or Generative AI technologies is considered an asset.
- Demonstrated commitment to engineering best practices, collaboration, and continuous learning.
Responsibilities
- Lead the design, architecture, and implementation of enterprise-scale Azure data platforms using Microsoft Fabric, Azure SQL, Power BI, and modern Data Lakehouse architectures.
- Partner with stakeholders to gather business requirements and translate them into scalable, secure, and high-performing data solutions.
- Design and optimize ETL/ELT pipelines, semantic models, reporting layers, dashboards, and analytics solutions.
- Drive migration initiatives from legacy data platforms to modern cloud-based environments while ensuring seamless data integration.
- Establish governance standards for data quality, privacy, security, compliance, and long-term data retention.
- Build AI-ready data pipelines that support advanced analytics, machine learning, and Generative AI use cases.
- Mentor data engineers through technical leadership, code reviews, best practices, and knowledge sharing.
- Collaborate with cross-functional engineering, product, and business teams to align data strategies with organizational objectives.
- Monitor platform performance, troubleshoot complex data challenges, and continuously improve reporting and analytics capabilities.
- Promote innovation by evaluating and implementing new cloud technologies, frameworks, and AI-assisted development practices.
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