\\\\Introduction\\\\ A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. Youll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, youll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. Youll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. \\\\Your role and responsibilities\\\\ As a Data Engineer specializing in Data Platforms on Azure, you will advise on, develop, and maintain data engineering solutions on the Azure Cloud ecosystem. You will design, build, and operate batch and real-time data pipelines using various Azure services. Your primary responsibilities will include: Design and Build Data Pipelines: Design, build, and operate batch and real-time data pipelines using Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub. Develop Data Layer: Design, build, and operate the data layer on Azure Synapse Analytics, SQL DW, and Cosmos DB. Apply Azure Expertise: Apply proficiency in Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB. Implement Open Source Technologies: Implement open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions. Operate Azure Services: Operate Azure services such as Azure Event Hub and Streaming Analytics, Managed Streaming for Apache Kafka, and Azure DataBricks with Spark. \\\\Required technical and professional expertise\\\\ Exposure to Azure Toolset: Experience working with Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub to design, build, and operate batch and real-time data pipelines. Proficiency in Azure Data Platform: Exposure to Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB. Data Pipeline Development: Experience working with batch and real-time data pipelines using Azure services, including designing, building, and operating data pipelines. Open Source Technologies: Exposure to open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions. Azure Services Operation: Experience working with Azure services such as Azure Event Hub and Streaming Analytics, Managed Streaming for Apache Kafka, and Azure DataBricks with Spark. \\\\Preferred technical and professional experience\\\\ Proficiency in Open Source Technologies: Exposure to open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions. Knowledge of Azure Services: Experience working with Azure services such as Azure Event Hub and Streaming Analytics, Managed Streaming for Apache Kafka, and Azure DataBricks with Spark. Familiarity with Data Pipeline Development: Exposure to designing, building, and operating batch and real-time data pipelines using Azure services. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

The role requires experience working with Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub for designing, building, and operating batch and real-time data pipelines. Proficiency in Azure Data Platform components including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB is necessary. Experience with open-source technologies like Apache Airflow and dbt, Spark/Python, or Spark/Scala for data engineering solutions is also required. Additionally, experience operating Azure services such as Azure Event Hub and Streaming Analytics, Managed Streaming for Apache Kafka, and Azure DataBricks with Spark is needed.