Back
Mid-level seniority with a common Data Engineer title and metro location yields moderate applicant density.
Data engineering skills transfer across industries but Azure-centric tooling raises moderate domain specificity.
Explicit 8–12 years requirement plus mandatory Azure Data Factory, Databricks, and Synapse skills increases filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design, architecture, and implementation of enterprise-scale data infrastructure and end-to-end data pipelines primarily within Microsoft Azure.
Develop, optimize, and maintain ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, and Databricks to support analytics and business intelligence.
Ensure data solutions are scalable, secure, performant, and cost-efficient while collaborating with cross-functional teams and driving modern data platform initiatives such as Data Lakehouse architectures.
8–12 years of professional experience in data engineering or data platform development roles.
Bachelor’s degree in Computer Science, IT, Engineering, or related discipline.
Hands-on experience with Azure Data Services including Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Data Lake Storage in production environments.
Proficiency in SQL and data pipeline development with knowledge of data modeling, resource optimization, cost management, and data security in Azure.
Experienced technical lead comfortable driving architecture and implementation of large-scale, enterprise Azure data platforms.
Skilled in collaborating with global, cross-functional teams to align priorities and deliver high-impact data solutions on schedule.
Strong analytical and problem-solving skills demonstrated by ability to troubleshoot complex pipelines, analyze performance metrics, and optimize resource utilization.