Senior Real-Time Data Engineer

Senior Real-time Data Engineer to lead the architecture and development of our customer-facing analytics engine. Our Sales Engagement Platform processes millions of events dailyfrom email interactions to live call metadata. Your mission is to transform this firehose of raw data into high-performance, actionable insights for our users. You will bridge the gap between backend data systems and the user interface, building a robust Semantic Layer that ensures our customers see the same "Single Source of Truth" across every dashboard, report, and API. 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. Technical Requirements: OLAP Expertise: - 3+ years of experience with Apache Pinot (or similar technologies like ClickHouse/StarRocks) in a production environment. Semantic Modeling: - Deep experience with Cube (Cube.js), including advanced features like pre-aggregations, security contexts, and multi-tenant configurations. Data Store Mastery: - Expert-level knowledge of PostgreSQL, specifically in the context of analytical query optimization and Change Data Capture (CDC). Streaming & Ingestion: - Hands-on experience with real-time data movement (Debezium, Kafka, or Flink). Software Craftsmanship: - Proficiency in Node.js or Python, with a focus on building scalable backend services. Language: - Mastery of complex SQL and the ability to translate business logic into code-based data schemas. Bonus Points If You Have: - Experience building analytics specifically for CRM or Sales Tech ecosystems. - Contributions to open-source projects (specifically in the Pinot or Cube communities). - Experience with Infrastructure as Code (Terraform, Kubernetes) for managing data clusters.