Data Engineer

About the Role We’re looking for a Data Engineer to design, build, and maintain scalable data pipelines, ETL processes, and data models. This role includes optimizing data storage, ensuring data quality, and collaborating with cross-functional teams to deliver impactful, data-driven solutions. Key Responsibilities Design, build, and maintain AI and analytics solutions on Databricks, including its native AI/ML capabilities.

Develop AI agents and agentic workflows to automate analysis, reporting, and decision-support tasks. Build and curate AI-driven BI insights using Databricks Genie (Workspace) for natural-language analytics and integrate outputs with Power BI dashboards. Translate NBFC business problems (loan origination, credit assessment, delinquency/collections, portfolio monitoring, KYC/AML, regulatory reporting) into AI/BI solutions.

Implement and work with the Model Context Protocol (MCP) and related tooling to connect LLMs/agents to internal data sources and enterprise tools. Collaborate with business, risk, and data teams to identify high-value AI use cases and deliver measurable outcomes. Ensure solutions adhere to data governance, security, and applicable regulatory requirements (RBI guidelines, data privacy).

Required Skills & Experience 4-6 years of relevant experience in AI/ML, data engineering, or BI/analytics. Hands-on experience with Databricks and its AI use cases (Lakehouse, notebooks, ML workflows; familiarity with Mosaic AI / Databricks AI features is a plus). Practical knowledge of AI and AI agents — building, deploying, and orchestrating LLM-based and agentic solutions.

Experience generating AI-driven BI insights , including Databricks Genie / Genie Workspace for natural-language querying and Power BI for visualization and reporting. Working knowledge of the Model Context Protocol (MCP) and associated tools for connecting AI agents to data and applications. Understanding of Indian NBFC / financial services use cases and the data involved (lending, credit bureau/CIBIL data, collections, regulatory reporting).

Strong SQL and Python; solid grasp of data modeling and pipelines.