Inclusion Cloud
About the job: Azure Data Engineer
About the client The client is an American automotive components original equipment manufacturer and an aftermarket ride control and emissions products manufacturer. It is a
Fortune
500 company located in Detroit. Role and Responsibilities Design and build data processing components and systems utilizing cloud compute technologies including Azure Cloud. Design requirement-driven data models such as dimensional, relational, and data vault. Build ELT/ETL pipelines using workflow management applications such as SSIS, Azure Data Factory, and distributed computing platforms like Synapse and Databricks Spark. Acquire, analyze, combine, synthesize, and store data from a wide range of internal and external sources. Experience with SAP systems is a plus. Build and test CI/CD deployment pipelines for data system components. Develop and support monitoring solutions for data systems and components. Partner with business and technical leaders to prioritize data needs and expand analytics capabilities. Qualifications and Education Requirements 5+ years of experience in data engineering, data warehousing, or related fields. Understanding of data lake design and implementation is a plus. Mastery of SQL and experience with data warehouse design and implementation across various domains such as Sales & Distribution, Supply Chain, Finance, and Marketing. Experience with SAP VMWare is a plus. Experience building and automating data system components for data acquisition, cleansing, and persistence engineering. Proficiency with Azure technologies such as Azure Blob Storage, Azure Data Lake Storage Gen2, and Azure SQL Database. Experience with visualization tools like Power BI is preferred. Monitoring performance of data analysis and system components. Versioning of data snapshots, data lineage, schemas, and database systems. Deployment through CI/CD pipelines, preferably with Azure DevOps. Strong PowerShell and cloud compute skills. Experience with analytics tools such as Databricks Spark and PySpark. Experience with infrastructure as code tools like Terraform. Knowledge of modern CI/CD practices involving git, static code analysis, and test-driven development. Experience across the full data engineering lifecycle, from business understanding to operational system development. Awareness of regulations around PII data. Understanding of various database models and DBMSs. Excellent problem-solving, communication, and teamwork skills. Big picture thinking to incorporate business insights into design. Consulting experience, including client-facing communication, is a plus. Ability to develop timely solutions for complex design problems. Ability to establish relationships with data owners, experts, and SMEs to expand analytics capabilities.
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About the client The client is an American automotive components original equipment manufacturer and an aftermarket ride control and emissions products manufacturer. It is a
Fortune
500 company located in Detroit. Role and Responsibilities Design and build data processing components and systems utilizing cloud compute technologies including Azure Cloud. Design requirement-driven data models such as dimensional, relational, and data vault. Build ELT/ETL pipelines using workflow management applications such as SSIS, Azure Data Factory, and distributed computing platforms like Synapse and Databricks Spark. Acquire, analyze, combine, synthesize, and store data from a wide range of internal and external sources. Experience with SAP systems is a plus. Build and test CI/CD deployment pipelines for data system components. Develop and support monitoring solutions for data systems and components. Partner with business and technical leaders to prioritize data needs and expand analytics capabilities. Qualifications and Education Requirements 5+ years of experience in data engineering, data warehousing, or related fields. Understanding of data lake design and implementation is a plus. Mastery of SQL and experience with data warehouse design and implementation across various domains such as Sales & Distribution, Supply Chain, Finance, and Marketing. Experience with SAP VMWare is a plus. Experience building and automating data system components for data acquisition, cleansing, and persistence engineering. Proficiency with Azure technologies such as Azure Blob Storage, Azure Data Lake Storage Gen2, and Azure SQL Database. Experience with visualization tools like Power BI is preferred. Monitoring performance of data analysis and system components. Versioning of data snapshots, data lineage, schemas, and database systems. Deployment through CI/CD pipelines, preferably with Azure DevOps. Strong PowerShell and cloud compute skills. Experience with analytics tools such as Databricks Spark and PySpark. Experience with infrastructure as code tools like Terraform. Knowledge of modern CI/CD practices involving git, static code analysis, and test-driven development. Experience across the full data engineering lifecycle, from business understanding to operational system development. Awareness of regulations around PII data. Understanding of various database models and DBMSs. Excellent problem-solving, communication, and teamwork skills. Big picture thinking to incorporate business insights into design. Consulting experience, including client-facing communication, is a plus. Ability to develop timely solutions for complex design problems. Ability to establish relationships with data owners, experts, and SMEs to expand analytics capabilities.
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