Data Engineering Manager

#WeAreParamount 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. Data Engineering Manager The Applied Intelligence Data Engineering team is seeking a Data Engineering Manager to lead the design, delivery, and operational excellence of large-scale batch and real-time data platforms. This role will oversee high-performance data engineering teams that power analytics, APIs, AI workflows, and mission-critical data services across the organization. You will guide the design and execution of distributed data systems. This includes ETL and ELT pipelines. It also includes streaming platforms, warehouse ecosystems, and cloud-native infrastructure. As a manager, you will shape technical direction, develop engineering talent, and ensure production-grade reliability, scalability, and operational rigor across data platforms. This role requires expertise in modern data architectures and distributed systems. You should also have knowledge of cloud-native engineering. It is essential to have proven experience in leading high-performing engineering teams. Key Responsibilities Lead & Scale Data Engineering Teams ● Manage and develop a team of data engineers responsible for batch and streaming data systems. ● Drive technical execution across ingestion, transformation, modeling, and serving layers. ● Establish engineering standards, code quality practices, and architectural review processes. ● Mentor engineers in distributed systems design, performance optimization, and production reliability. Architect Scalable Data Platforms ● Ensure scalable data modeling standards and efficient query performance across analytical workloads. ● Guide decisions about architecture. This includes orchestration and schema evolution. It also covers partitioning strategies and storage optimization. ● Partner with streaming engineers to align batch and real-time data patterns into cohesive platform designs. Cross-Functional Collaboration ● Partner closely with Data Product Management to align roadmap priorities, SLAs, and platform KPIs. ● Collaborate with software engineers to integrate streaming systems, APIs, and microservices into the broader data ecosystem. ● Clearly communicate architectural tradeoffs to stakeholders.