"The data engineering landscape is booming as companies modernize their supply chains with real\u2011time analytics. Roles that blend cloud ETL expertise with emerging AI technologies are in high demand, making this junior position a perfect launchpad. This opportunity lets you work remotely while gaining hands\u2011on experience with Azure, Kafka, and generative AI concepts.\n\n# Job Summary\nWe are seeking a Junior Data Engineer to design, build, and maintain scalable ETL/ELT pipelines on Azure, support supply\u2011chain data workflows, and collaborate with analytics teams. The role requires solid SQL background (IBM DB2, SQL Server, Oracle), exposure to streaming technologies, and a curiosity for MLOps and Generative AI concepts.\n\n# Top 3 Critical Skills Table\n| Skill | Why it's critical | Mastery Level |\n|-------|-------------------|--------------|\n| Azure Data Factory / Databricks | Core platform for building and orchestrating cloud\u2011native pipelines | Mid |\n| Kafka (or similar streaming tool) | Enables real\u2011time inventory and logistics data flow | Mid |\n| MLOps principles | Allows productionizing ML models that forecast demand and optimize operations | Junior |\n\n# Interview Preparation\n1. **Design an end\u2011to\u2011end ETL pipeline in Azure Data Factory for inventory data.**\n *What the interviewer is looking for:* Understanding of linked services, datasets, copy activities, scheduling, error handling, and how to integrate with Azure Databricks for transformations.\n2. **Compare Kafka with Azure Event Hubs and explain when you would choose each.**\n *What the interviewer is looking for:* Knowledge of streaming architectures, throughput, durability, ecosystem integrations, and cost considerations.\n3. **Describe how you would implement MLOps for a demand\u2011forecasting model.**\n *What the interviewer is looking for:* Experience with CI/CD pipelines, model versioning, automated testing, monitoring, and rollback strategies.\n4. **Walk through the steps you take to optimize a slow query in IBM DB2 or SQL Server.**\n *What the interviewer is looking for:* Ability to analyze execution plans, use indexes, rewrite joins, and apply partitioning or statistics.\n5. **What are the key considerations when integrating Generative AI (LLMs) into a supply\u2011chain workflow?**\n *What the interviewer is looking for:* Awareness of prompt engineering, data privacy, latency, model governance, and realistic use\u2011case boundaries.\n\n# Resume Optimization\n- Junior Data Engineer\n- Azure Data Factory\n- Azure Databricks\n- ETL / ELT\n- Kafka\n- IBM DB2\n- SQL Server\n- MLOps\n- Generative AI\n- Supply chain analytics\n\n# Application Strategy\nWhen reaching out to the recruiter, send a concise email that starts with a friendly greeting, attaches your updated resume, and clearly highlights your top skills. Make sure to mention related skills you possess, such as Azure Data Factory, Kafka streaming, and MLOps practices, and reference any supply\u2011chain or inventory projects that align with the role.\n\n# Career Roadmap\n| Current Role | Typical Experience | Core Focus | Next Position |\n|--------------|-------------------|------------|---------------|\n| Junior Data Engineer | 0\u20112 years | Build ETL pipelines, learn Azure services, support data ingestion | Data Engineer II |\n| Data Engineer II | 2\u20114 years | Own end\u2011to\u2011end pipelines, introduce MLOps, optimize performance | Senior Data Engineer |\n| Senior Data Engineer | 4\u20117 years | Architect solutions, lead projects, mentor junior staff | Data Engineering Manager |\n"