Role : Lead Data Engineer

Experience : 10 - 12 yrs

Location : Chennai / Bangalore

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.

- Familiarity with containerization using Docker and orchestration with Kubernetes for deploying and managing data workloads.

- 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.