Director Data Engineering

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 (Py.

Spark, 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