Data Engineering Lead - ETL Workflows

Role : Lead Data Engineer Responsibilities : - Design and build scalable, reliable data pipelines and ETL/ELT workflows across batch and near-real-time processing scenarios. - Develop and maintain data models across relational (SQL Server) and NoSQL (MongoDB, Atlas) systems to support analytical and operational use cases. - Build and manage data solutions on Microsoft Fabric including Lakehouses, Warehouses, Dataflows, and Pipelines to deliver a unified analytics platform. - Drive AI-enabled development practices within the team leveraging AI coding assistants, LLM-based tools, and intelligent automation to accelerate pipeline development, improve code quality, and reduce manual effort. - Lead, mentor, and grow a team of data engineers; conduct code reviews, provide technical guidance, and support career development. - Collaborate with stakeholders, product owners, data scientists, and analysts to understand data needs and translate them into engineering solutions. - Build and maintain Power BI data models, semantic layers, and datasets to support self-service analytics and business reporting. - Continuously identify opportunities to optimize pipeline performance, reduce costs, and improve data reliability and quality. - Adhere to and enforce data engineering standards, data security, and governance practices across the platform. Required Skills and Experience : - 8 to 10 years of experience in data engineering with a strong track record of delivering production-grade data solutions. - Strong proficiency in Python (PySpark, Pandas) and SQL for data transformation, pipeline development, and performance tuning. - Proficient with Microsoft Fabric including Lakehouses, Warehouses, Dataflows Gen2, and Data Pipelines. - Strong experience with SQL Server including schema design, stored procedures, indexing, and query optimization. - Experience with MongoDB and MongoDB Atlas for NoSQL data modeling, indexing, aggregation pipelines, and Atlas Search. - Solid experience designing and operating ETL/ELT pipelines in production, including error handling, monitoring, and SLA management. - Experience with Power BI including dataset design, DAX, semantic modeling, and enabling self-service reporting. - Strong exposure to AI-enabled development using AI coding assistants, prompt-driven development, or LLM-integrated tooling to build and accelerate data engineering workflows. - Experience leading or managing a small team of engineers task allocation, mentoring, and performance support. - Good understanding of data modeling concepts dimensional modeling, star/snowflake schemas, data vault. - Ability to communicate technical ideas clearly to both technical and non-technical audiences. Nice to Have Qualities & Skills : - Hands-on experience with Databricks including Delta Lake, notebooks, jobs, clusters, and Unity Catalog. - Exposure to cloud data services on Azure (preferred), GCP, or AWS. - Experience with streaming and event-driven architectures using Apache Kafka, Azure Event Hubs, Azure Service Bus, or similar queue/messaging technologies. - Exposure to .NET for building data-adjacent services or APIs. - Familiarity with data governance, data cataloging, and data lineage tooling. - Exposure to MLOps or supporting ML pipeline infrastructure. - Exposure to Mortgage or Real Estate domain.