Sr. Software Engineer Data Streaming Systems

#We Are Paramount  on a mission to unleash the power of content… you in? We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness.

Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

It will build fault-tolerant streaming applications supporting real-time analytics, APIs, AI workflows, and critical data services. This role will design and build high-performance, fault-tolerant streaming applications that power real-time analytics, APIs, AI workflows, and mission-critical data services across the organization. You will architect and implement distributed, event-driven systems using Java, Kafka, Kubernetes, and modern reactive frameworks. As a senior engineer, you will set the technical direction.

You will mentor other engineers. You will also ensure that our streaming platforms are reliable, scalable, and operate smoothly. This role requires deep expertise in distributed systems, concurrency, and cloud-native microservices.

Design & Build Real-Time Streaming Applications

• Develop high-throughput, low-latency streaming applications using Java and Kafka.
• Design event-driven microservices that process, enrich, and route real-time data at scale.
• Implement reactive, non-blocking architectures to support high concurrency and resilience. Create reusable streaming frameworks and libraries. These will help improve engineering efficiency and standardization across the platform.

Architect Scalable Distributed Systems

• Design and optimize distributed streaming architectures. You will work with Kafka topics, partitioning strategies, consumer groups, schema management, and event lifecycle patterns.
• Help make architecture decisions for the whole platform. This includes scalability, resiliency, high availability, disaster recovery, and deployments across multiple regions.
• Drive best practices around event modeling, schema evolution, idempotency, replayability, and data consistency across streaming systems.
• Build and optimize horizontally scalable services deployed within Kubernetes-based cloud environments.
• Production Reliability & Performance Make sure that streaming platforms and services are reliable and easy to monitor. Keep these systems operationally mature.
• Optimize systems for throughput, latency, resiliency, resource efficiency, and infrastructure cost management. Set up full observability using metrics and centralized logging. Use distributed tracing, alerting, and health monitoring tools.
• Build automated testing strategies for streaming workflows, including unit, integration, contract, chaos, and performance testing.
• Participate in production support. Help with incident response and root-cause analysis. Work on initiatives to improve reliability continuously.

Cloud-Native & Kubernetes Engineering

• Deploy and manage containerized microservices within Kubernetes environments across GCP, AWS, or similar cloud platforms.
• Define strategies for autoscaling, deployment, failover, and resource optimization. These strategies will focus on high-volume production systems.
• Create and manage CI/CD pipelines. This includes Infrastructure-as-Code and workflows for automated deployment.
• Collaborate with platform engineering teams to improve developer tooling, deployment automation, and runtime reliability.

Cross-Functional Collaboration

• Partner with Data Engineering teams to integrate streaming architectures with batch processing systems, data lakes, and analytical platforms.
• Collaborate with Software Engineering, Product Management, and API teams to enable real-time services and data-driven applications.
• Work closely with AI/ML engineering teams to support real-time feature engineering, inference pipelines, and operational AI workloads.
• Clearly explain the tradeoffs of different technical options. Discuss scalability considerations and operational risks with engineering stakeholders.