Back
Strong employer brand, mid-senior generalist data role, and likely metro hiring increase applicant competition.
Data engineering skills are transferable across industries but Azure/Fabric-specific platform experience raises required domain fit.
Explicit 6–10 years requirement plus mandatory Azure, Fabric, PySpark and data platform expertise makes filters stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and delivery of enterprise-scale data pipelines and ETL/ELT solutions using Microsoft Fabric, Azure Data Factory, and PySpark.
Own architecture, optimization, and operational excellence of modern data warehousing and cloud data platforms on Azure.
Provide technical leadership including standards enforcement, mentoring, root-cause analysis, and driving continuous improvement for data engineering solutions.
6–10 years of progressive experience in Data Engineering with ownership of large-scale data platforms and technical leadership.
Strong expertise in advanced SQL (query optimization, performance tuning), data warehousing architecture, dimensional modeling, and scalable analytical data design.
Hands-on experience with Microsoft Fabric, Azure Data Factory, Azure Portal and Azure data services for environment management, monitoring, security, and cost optimization.
Bachelor’s degree in Computer Science, Information Technology, or related field.
Experienced in leading end-to-end cloud data platform implementations, balancing architectural vision with hands-on execution.
Strong collaborator able to partner with business and technical stakeholders to translate complex requirements into scalable data models and integration patterns.
Capable of establishing engineering standards, reusable frameworks, and driving operational excellence across cloud-based data pipelines and distributed workloads.