Key Responsibilities :

- Architecture & Scaling : Design and maintain a low-latency analytics stack capable of handling high-concurrency queries from thousands of concurrent SaaS users.

- Real-time Ingestion : Build and optimize ingestion pipelines that move data from transactional databases (PostgreSQL) and event streams (Kafka/Kinesis) into Apache Pinot.

- Semantic Layer Development : Use Cube (Cube.js) to model complex sales metrics (e.g., 'Sequence Conversion Rate,' 'Attributed Revenue,' 'Meeting Booked Rate') ensuring consistency across the application.

- Performance Engineering : Optimize Apache Pinot tables, indexing strategies, and Cube pre-aggregations to ensure dashboard widgets load in under 300ms.

- API Strategy : Expose data models via REST/GraphQL APIs, collaborating closely with Frontend Engineers to build world-class data visualizations.

- Data Governance : Implement multi-tenant security logic within the semantic layer to ensure strict data isolation between different customer accounts.

Required Skillset :

- Demonstrated expertise in building and managing OLAP systems and multi-dimensional data structures.

- Advanced proficiency in Python for data pipeline automation and SQL for complex query optimization and database management.

- Hands-on experience with high-performance analytical databases such as Clickhouse, StarRocks, and PostgreSQL.

- Ability to communicate technical architectural decisions clearly to non-technical stakeholders and cross-functional team members.

- Strong problem-solving mindset with the ability to troubleshoot performance bottlenecks in distributed data environments.

- Proven track record of working effectively in a hybrid work environment, balancing independent technical contribution with collaborative team goals.

- Candidates must possess 6 to 11 years of relevant experience in data engineering or a closely related technical domain.