Ranking: 37
As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data pipelines and applications within the Azure ecosystem. You will work closely with cross-functional teams to enable seamless data flow, optimize processing efficiency, and implement scalable architectures tailored to business needs.
This role requires a strong foundation in Python, distributed computing, and Azure data services.
Design, build, and maintain large-scale data processing pipelines using Spark and Azure technologies.
Develop data-driven applications with a focus on performance, scalability, and reliability.
Implement and optimize ETL/ELT workflows within Azure Synapse, Data Factory, and related services.
Work with stakeholders to understand data requirements and translate them into efficient engineering solutions.
Ensure data quality, governance, and compliance across all data processes.
Troubleshoot production pipelines, monitor performance, and apply optimizations when necessary.
Collaborate with analytics, cloud, and product teams to enable end-to-end data delivery.
Python (advanced) + PySpark + Pandas
Strong understanding of Spark and distributed data processing concepts
SQL expertise
Hands-on experience with:
Azure Synapse Analytics
Azure Data Factory
Azure Data Services (core components)
Experience with Azure Functions, Azure Virtual Machines, and Azure DevOps
Familiarity with CI/CD pipelines and Infrastructure as Code (ARM templates)
Knowledge of data modeling, ETL frameworks, and data governance best practices
As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data pipelines and applications within the Azure ecosystem. You will work closely with cross-functional teams to enable seamless data flow, optimize processing efficiency, and implement scalable architectures tailored to business needs.
This role requires a strong foundation in Python, distributed computing, and Azure data services.
Design, build, and maintain large-scale data processing pipelines using Spark and Azure technologies.
Develop data-driven applications with a focus on performance, scalability, and reliability.
Implement and optimize ETL/ELT workflows within Azure Synapse, Data Factory, and related services.
Work with stakeholders to understand data requirements and translate them into efficient engineering solutions.
Ensure data quality, governance, and compliance across all data processes.
Troubleshoot production pipelines, monitor performance, and apply optimizations when necessary.
Collaborate with analytics, cloud, and product teams to enable end-to-end data delivery.
Python (advanced) + PySpark + Pandas
Strong understanding of Spark and distributed data processing concepts
SQL expertise
Hands-on experience with:
Azure Synapse Analytics
Azure Data Factory
Azure Data Services (core components)
Experience with Azure Functions, Azure Virtual Machines, and Azure DevOps
Familiarity with CI/CD pipelines and Infrastructure as Code (ARM templates)
Knowledge of data modeling, ETL frameworks, and data governance best practices
