Sr. Software Engineer, Data Streaming Systems

Sr. Software Engineer, Data Streaming SystemsOn 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.Overview: 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.Primary ResponsibilitiesDesign & Build Real-Time Streaming ApplicationsDevelop 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 SystemsDesign and optimize distributed streaming architectures.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 & PerformanceMake 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 EngineeringDeploy 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 CollaborationPartner 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.Required Technical SkillsJava & Reactive ProgrammingAdvanced proficiency in Java, including concurrency, multithreading, and JVM performance tuning.Strong experience with reactive frameworks such as Spring WebFlux, Project Reactor, or similar.Deep knowledge of asynchronous, non-blocking system design.Kafka & Event-Driven ArchitectureExtensive experience with Apache Kafka (producers, consumers, streams, schema registry).Strong knowledge of partitioning strategies, offset management, rebalancing, and failure recovery.Experience designing event schemas and managing schema evolution.Familiarity with Kafka Streams, Flink, or similar stream-processing frameworks.Kubernetes & Cloud-Native SystemsStrong hands-on experience deploying and operating applications in Kubernetes.You should have experience using Docker for containerization.It's important to have knowledge of microservices architecture.You should understand how autoscaling functions. You also need to learn about rolling deployments.It's important to know patterns that ensure reliable production.Basic Qualifications5+ years of professional software engineering experience, including 2+ years working on streaming or distributed systems.You should have experience in designing, building, and running production-grade real-time systems. This includes event-driven systems.You should be comfortable working in cloud-based systems that use microservices. This includes engineering environments that focus on these technologies.Ability to take ownership of complex technical work and deliver reliable solutions autonomously.Willingness to mentor junior engineers and contribute to team engineering standards.Strong communication and collaboration skills across engineering and product teams.Bachelor's degree in Computer Science, Engineering, or related technical field; advanced degree preferred.