Around 90% of India’s ~$1 trillion retail market is controlled by Micro, Small & Medium Enterprises (MSMEs). At Khatabook, we envision empowering MSMEs and helping them increase their incomes. We have built a product that brings efficiency to MSME operations by providing easy-to-use tools to manage receivables, inventory, and billing, creating transparency in cash flow. We have also enabled access to short-term working capital loans for select users demonstrating good credit behavior.

People are our biggest asset! We, at Khatabook, are looking to hire a Data Engineer II for our Data Engineering team.

As a Data Engineer II, you will build, maintain, and scale reliable, secure, and cost-efficient data infrastructure that enables data-driven decision-making across the organisation. You will work on production-grade data pipelines, cloud data warehouses, CDC systems, streaming platforms, and large-scale data processing.

You will play a key role in ensuring data reliability, quality, security, and performance while contributing to AI-ready data infrastructure that supports AI/ML and Generative AI use cases.

Design, develop, and maintain scalable batch and streaming data pipelines using Python and Apache Airflow.

Build reliable data ingestion workflows with appropriate error handling, retries, monitoring, and recovery mechanisms.

Develop incremental data processing workflows and Change Data Capture (CDC) pipelines to ensure timely and consistent data availability.

Build, maintain, and optimize cloud data warehouses, preferably Snowflake, with a focus on scalability, performance, and cost efficiency.

Develop and maintain SQL-based data models and transformation workflows using tools such as DBT.

Build event-driven data workflows using Kafka or other messaging and queuing technologies.

Develop and optimize Apache Spark jobs for large-scale data processing.

Build and operate data engineering workloads on AWS and work with Kubernetes and containerised environments where appropriate.





Optimize SQL queries, data models, Spark jobs, warehouse utilisation, and pipeline execution to improve performance and reduce infrastructure costs.

Implement data quality checks, validation frameworks, monitoring, alerting, and failure-recovery mechanisms to ensure data accuracy, freshness, and reliability.

Implement data access controls, role-based access control (RBAC), secure data sharing, and appropriate security practices for sensitive data.

Support data governance, metadata management, and data lineage to improve data discoverability and trust.

Build and maintain data pipelines that prepare structured and unstructured data for AI/ML and Generative AI applications.

Support AI-powered search and analytics use cases involving Large Language Models (LLMs), embeddings, vector databases, and Retrieval-Augmented Generation (RAG).

Collaborate with engineering, product, and analytics teams to understand data requirements and deliver trusted, actionable data.

Take ownership of production workloads, troubleshoot pipeline failures, investigate incidents, and continuously improve data platform reliability and operational efficiency.

Explore AI-assisted development tools to improve Python and SQL development, debugging, and code quality while validating generated code for correctness and security.

Participate in a shared production on-call rotation approximately once every three weeks to ensure the reliability of critical data workflows.

You have 3–7 years of experience in Data Engineering or a related technical role.





You have strong Python programming skills and advanced SQL knowledge, including complex joins, window functions, query optimisation, and performance tuning.

You have hands-on experience building and maintaining production-grade data pipelines using Apache Airflow.

You have experience working with modern data warehouses such as Snowflake or Amazon Redshift.

You have experience with CDC pipelines, incremental data ingestion, or database replication.

You have working knowledge of AWS cloud services and experience operating data engineering workloads.

You have experience with Apache Spark or PySpark for large-scale data processing.

You have experience with Kafka or other messaging and queuing technologies.

You have a strong understanding of relational databases such as PostgreSQL or MySQL.

You understand data modeling, data quality, pipeline reliability, monitoring, troubleshooting, and production support.

You understand distributed systems, data security, and performance optimisation.

You can collaborate effectively with engineering, product, and analytics teams to deliver scalable data solutions.

Strong knowledge of Snowflake security, RBAC, secure data sharing, and compute and storage cost optimisation.

Familiarity with DBT for data transformation, testing, documentation, and data model management.

Understanding of dimensional modeling and contemporary data warehouse architecture.

Familiarity with Docker, Kubernetes, CI/CD, and automated testing.

Experience with data observability, metadata management, and data lineage.

Understanding of Generative AI concepts, including LLMs, embeddings, vector databases, and RAG.

Exposure to AI-ready data pipelines for structured and unstructured data.

Familiarity with AI-assisted development tools for Python and SQL.

Exposure to AI application platforms such as AWS Bedrock or Snowflake Cortex.

Familiarity with BI and analytics platforms such as Tableau, Redash, or similar tools.

📌 MySQL Data Platforms Engineer (Bengaluru)
🏢 Khatabook
📍 Bengaluru