Data Engineering Lead
First seen by Alion on Sep 12, 2026.
About Kadel Labs :
Kadel Labs is a leading IT services company delivering top-quality technology solutions since 2017, focused on enhancing business operations and productivity through tailored, scalable, and future-ready solutions. With deep domain expertise and a commitment to innovation, we help businesses stay ahead of technological trends. As a CMMI Level 3 and ISO 27001:2022 certified company, we ensure best-in-class process maturity and information security, enabling organizations to achieve their digital transformation goals with confidence and efficiency.
Role : Lead Data Engineer
Location: Bangalore/Chennai
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 :
- 7 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.
Skills
Microsoft Fabric, Python, SQL Server, MongoDB, ETL, Data Modeling, Databricks, Azure, Kafka, Data Engineering