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Metro location and popular Data Engineer title increase competition, while niche Pinot/Cube.js skills limit applicant pool.
Highly domain-specific OLAP, real-time ingestion, and Cube.js/Pinot expertise limits cross-industry transferability.
Explicit 7 to 12 years and mandatory Pinot, Cube.js, streaming, and semantic-layer skills enforce strict filters.
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Lead architecture and development of a scalable, low-latency real-time analytics engine processing millions of daily events for a Sales Engagement Platform.
Design and optimize ingestion pipelines from PostgreSQL and event streams (Kafka/Kinesis) into Apache Pinot for high concurrency querying.
Build and maintain the semantic layer using Cube.js to ensure consistent, secure, multi-tenant metric modeling and expose data via REST/GraphQL APIs for user-facing dashboards.
7 to 12 years of professional experience in data engineering or related roles.
3+ years of hands-on production experience with Apache Pinot or similar OLAP technologies (ClickHouse/StarRocks).
Deep expertise with Cube.js semantic modeling including pre-aggregations and multi-tenant security.
Strong proficiency in PostgreSQL analytical query optimization, real-time data ingestion (Kafka, Debezium, Flink), and backend programming in Node.js or Python.
Experienced with designing high concurrency, real-time analytics systems supporting thousands of concurrent SaaS users.
Skilled in building robust semantic layers bridging backend data and frontend analytics with a strong focus on consistency and security.
Background in Sales or CRM analytics preferred, with prior exposure to infrastructure automation (Terraform, Kubernetes) and open-source community contributions.