Azure Data Engineer - Con - MFT KGS CH

Job description

**Job Description** Azure Data Engineering is responsible for leading the design, delivery, and operational excellence of enterprise-scale data platforms on Microsoft Azure. This role combines hands-on technical leadership, team management, and stakeholder engagement, with a strong focus on enabling AI and Generative AI (GenAI) initiatives through high-quality, secure, and reliable data pipelines. The Assistant Manager oversees data engineering teams, defines technical standards, and ensures that data platforms effectively support LLM training, embeddings, inference pipelines, and AI-enabled applications, while meeting enterprise security, governance, and performance expectations. Interact with multiple stakeholders and manage the end-to-end data project delivery from project requirements, solutioning and execution and hand off to operations. **Responsibilities** - Own the architecture, design, and delivery of scalable data storage and processing solutions using Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, and Azure Databricks. - Define and enforce data engineering standards, best practices, and design patterns across teams. - Provide technical leadership and oversight for complex ETL/ELT pipelines. - Lead data engineering support for AI and GenAI workloads, including preparation and management of training datasets, embeddings, and inference pipelines. - Partner with data scientists, AI engineers, and solution architects to meet AI performance and governance requirements. - Manage, mentor, and grow a team of Azure Data Engineers. - Plan capacity, allocate work, and ensure timely delivery of initiatives. - Drive adherence to Agile delivery practices. - Drive optimization of storage, compute, and pipelines for performance and cost efficiency. - Review and approve designs with long-term sustainability in mind. **Qualifications** - Bachelor’s degree in computer science, Engineering, Information Systems, or a related technical field, or equivalent practical experience. - Experience with Azure cloud services, specifically data storage and data processing solutions. - Strong knowledge of data warehousing and ETL (Extract, Transform, Load) concepts. - Hands‑on experience with SQL, Python and PySpark. - Understanding of GenAI and LLM data concepts, including training data, embeddings, and inference pipelines. - Strong understanding of data security and privacy best practice. - Experience with monitoring and troubleshooting data pipelines. - Strong problem‑solving and analytical skills. - Effective communication skills to collaborate with cross‑functional teams. - Experience with data governance is a plus. - Proven experience leading and mentoring teams. - Strong communication and stakeholder management skills. #KGS