Data Engineer-Data Platforms-Azure

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll 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, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll 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.
• Implement Azure Data Platform: Implement and maintain Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB.
• Utilize Open Source Technologies: Apply proficiency in open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions.
• Ensure Seamless Operations: Operate and maintain data engineering solutions to ensure seamless batch and real-time data processing.

Master's Degree

Required Technical And Professional Expertise

• Minimum 6 years in Data Engineering (Azure-focused). Only Development Experience required NO support Experience. 2-3 Implementation experience is required.
• Exposure to Azure Toolset: Proficiency in 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.
• Data Engineering on Azure: Experience working with Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB.
• Open-Source Technologies: Exposure to open-source technologies like Apache Airflow and dbt, PySpark / Python, or Spark to support data engineering solutions.Data Pipeline Development: Experience developing and maintaining data engineering solutions on the Azure Cloud ecosystem, including designing, building, and operating batch and real-time data pipelines.
• Azure Data Layer Design: Experience designing, building, and operating the data layer on Azure Synapse Analytics, SQL DW, and Cosmos DB
• Databricks Expertise: Hands-on experience with PySpark, Delta Live Tables, Unity Catalog, and performance optimization. SQL DB, Databricks, Pytest Analytics.
• Data Ingestion: Batch ingestion, Real-time ingestion, real-time processing, Streaming data pipelines, Event-driven ingestion, Incremental load, Full load, Delta load
• Data Processing Capabilities: Batch, real-time, and stream processing, Incremental, micro-batch, and event-driven processing
• Data Manipulation & Processing: Data cleansing, transformation, standardization, Data validation, quality checks, deduplication, reconciliation, Data mapping, merging, splitting, masking
• Data Integration: Strong hands-on in ETL & ELT pipelines, Data migration and transformation across systems, Data orchestration
• Data Modelling: Dimensional modelling, Star & Snowflake schema, CD Type 1 & Type 2,ER modelling (Entity Relationship Modelling)

• Proficiency in Azure Services: Exposure to 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.
• Open Source Technologies: Exposure to open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions.
• Data Engineering on Azure: Experience working with Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB.