JOB ID: QT-SNB-08-340

Job Summary

We are looking for a Senior Real-Time Data Engineer to lead the architecture and development of a high-performance, customer-facing analytics engine.

Our Sales Engagement Platform processes millions of events daily , including email interactions, call metadata, user activities, and other real-time engagement events. The successful candidate will transform this high-volume data into low-latency, actionable analytics for thousands of SaaS users.

You will work at the intersection of real-time data engineering, OLAP analytics, backend systems, and frontend data consumption , building a robust Semantic Layer that provides a consistent Single Source of Truth across dashboards, reports, and APIs.

Key Responsibilities 1. Architecture & Scalability

- Design and maintain a low-latency real-time analytics architecture capable of supporting high-concurrency queries.
- Build scalable data solutions capable of handling millions of events per day .
- Architect analytics systems for thousands of concurrent SaaS users.
- Identify and resolve performance, scalability, and reliability bottlenecks.
- Design systems with high availability, fault tolerance, and operational efficiency.

1. Real-Time Data Ingestion

- Build and optimize real-time ingestion pipelines from:
- PostgreSQL
- Apache Kafka
- Amazon Kinesis
- Event-driven systems

- Implement Change Data Capture (CDC) pipelines using technologies such as Debezium .
- Design reliable streaming data flows into Apache Pinot .
- Work with streaming technologies such as Kafka and/or Apache Flink .
- Ensure data freshness, correctness, ordering, and reliability across real-time pipelines.

1. Apache Pinot / OLAP Engineering

- Design, configure, and optimize Apache Pinot tables for high-performance analytical workloads.
- Develop appropriate:
- Table configurations
- Indexing strategies
- Partitioning strategies
- Segment configurations
- Query optimization techniques

- Troubleshoot slow queries and high-latency workloads.
- Optimize Pinot for high concurrency and real-time analytical use cases.
- Experience with alternative OLAP technologies such as ClickHouse or StarRocks is valuable.

1. Semantic Layer Development

- Build and maintain the organizations Semantic Layer using Cube (Cube.js) .
- Translate complex business requirements into reusable analytical data models.
- Develop metrics such as:
- Sequence Conversion Rate
- Attributed Revenue
- Meeting Booked Rate
- Engagement Rate
- Pipeline Conversion

- Ensure consistent metric definitions across dashboards, reports, APIs, and applications.
- Implement Cube pre-aggregations to improve query performance.
- Configure and manage:

- Pre-aggregations
- Security contexts
- Multi-tenant configurations
- Data access policies

- Establish a reliable Single Source of Truth for customer-facing analytics.

1. Performance Engineering

- Optimize Apache Pinot queries, tables, indexes, and ingestion pipelines.




- Design Cube pre-aggregations for frequently accessed analytical queries.
- Analyze query execution plans and identify performance bottlenecks.
- Work toward sub-300ms dashboard/widget response times .
- Optimize APIs and backend services for high-concurrency workloads.
- Continuously monitor and improve system latency, throughput, and resource utilization.

1. API & Backend Development

- Expose semantic models and analytics through REST and/or GraphQL APIs .
- Collaborate closely with Frontend Engineers to support data visualization requirements.
- Build scalable backend services using Node.js or Python .
- Design APIs optimized for real-time analytics and high-volume concurrent requests.
- Ensure APIs provide consistent and reliable analytical results.

1. Multi-Tenant Data Governance & Security

- Implement strict multi-tenant data isolation across the analytics platform.
- Design tenant-aware security logic within the Cube semantic layer.
- Ensure customers can access only their authorized data.
- Implement security contexts and row-level access controls where required.
- Work with engineering and security teams to maintain data governance and compliance.

1. Collaboration & Technical Leadership

- Partner with Backend, Frontend, Product, and Data teams to define analytical requirements.
- Translate business metrics into technical data models.
- Review architecture and code developed by other engineers.
- Drive technical decisions related to real-time analytics.
- Establish engineering best practices around data quality, performance, testing, and observability.
- Mentor junior and mid-level engineers.

Technical Requirements Mandatory Skills
- 6+ years of Data Engineering / Backend / Analytics Engineering experience
- 3+ years of Apache Pinot or equivalent OLAP technology
- Strong hands-on experience with Cube / Cube.js
- Expert-level PostgreSQL
- Advanced SQL
- Strong experience with Kafka / Kinesis
- Hands-on experience with CDC
- Experience with Debezium, Kafka and/or Flink
- Strong proficiency in Node.js or Python
- Experience designing and optimizing real-time data pipelines
- Experience building scalable analytics platforms
- Strong understanding of multi-tenant data architectures

Apache Pinot / OLAP

Strong production experience with

- Apache Pinot
- Pinot table design
- Indexing
- Partitioning
- Query optimization
- Real-time ingestion
- Segment management
- High-concurrency workloads

Experience with ClickHouse or StarRocks is also valuable.

Cube / Semantic Layer





Strong hands-on experience with:

- Cube.js
- Data schemas
- Measures and dimensions
- Pre-aggregations
- Security contexts
- Multi-tenant configurations
- Query optimization
- API exposure
- Semantic modeling

PostgreSQL
- Advanced SQL development
- Query optimization
- Indexing
- Execution plans
- Analytical query performance
- Transactional-to-analytical data movement
- Change Data Capture
- PostgreSQL performance tuning

Streaming & Data Engineering
- Apache Kafka
- Amazon Kinesis
- Debezium
- Apache Flink
- Event-driven architectures
- Real-time ingestion
- Streaming data pipelines
- Data quality and reliability

Programming
- Node.js or Python
- Strong software engineering fundamentals
- REST APIs
- GraphQL
- Automated testing
- Error handling and observability

Good to Have
- Experience building analytics platforms for CRM, Sales Engagement, MarTech, or SalesTech products.
- Experience with Terraform and Infrastructure as Code.
- Experience with Kubernetes .
- Experience managing distributed data/OLAP clusters.
- Experience with AWS cloud services.
- Contributions to open-source projects, particularly Apache Pinot or Cube .
- Experience designing customer-facing SaaS analytics platforms.
- Experience with observability and performance monitoring tools.

Preferred Experience Candidates with experience in the following environment will be highly preferred:

PostgreSQL Debezium Kafka/Kinesis Apache Pinot Cube.js Semantic Layer REST/GraphQL Customer-facing Dashboards

Key Competencies

- Strong system design and architectural thinking
- Excellent SQL and analytical problem-solving skills
- Strong understanding of distributed systems
- Performance and scalability mindset
- Ability to convert business requirements into analytical models
- Strong debugging and troubleshooting skills
- Excellent communication and collaboration
- Ownership of production systems
- Ability to work in a fast-paced SaaS workplace

Ideal Candidate The ideal candidate is a Senior Data/Analytics Engineer who has hands-on experience building real-time, customer-facing analytics platforms rather than only traditional batch ETL pipelines. They should be comfortable owning the complete analytics flow-from PostgreSQL and streaming events through Kafka/Debezium, Apache Pinot, Cube semantic modeling, API development, performance optimization, and multi-tenant security .

Core Skill Keywords

Apache Pinot | Cube.js | PostgreSQL | Kafka | Kinesis | Debezium | Flink | Node.js | Python | SQL | OLAP | CDC | Real-Time Data Engineering | Semantic Layer | REST | GraphQL | Multi-Tenancy | Pre-Aggregations | Query Optimization | Kubernetes | Terraform

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Real-Time Data Engineer (Nagpur)
🏢 Qloron Technology
📍 Nagpur