Explore Engineering Manager, CS Data Services jobs and discover a pivotal leadership role at the intersection of data infrastructure, software engineering, and customer support operations. Professionals in this career path lead teams responsible for building and maintaining the core data services and APIs that empower customer support organizations. Their work is foundational, enabling seamless, secure, and structured access to critical data—such as customer profiles, interaction histories, and case management information—which in turn powers agent tools, AI-driven support systems, operational analytics, and product integrations. Typically, an Engineering Manager for Customer Support (CS) Data Services oversees a team of software and data engineers. Their core mission is to develop a robust, scalable platform that serves as the single source of truth for support-related data. Common responsibilities include setting the technical vision and roadmap for the data services platform, prioritizing projects that enhance reliability and scalability, and fostering a culture of engineering excellence. They act as a crucial bridge, collaborating closely with product managers, data scientists, machine learning engineers, and other engineering teams to understand diverse data needs and deliver intuitive solutions. A significant part of the role involves mentoring engineers, managing project execution, and improving team processes to ensure high-quality, timely delivery of complex, cross-functional initiatives. Individuals seeking Engineering Manager, CS Data Services jobs generally possess a strong blend of technical depth and leadership acumen. Typical requirements include a substantial background in software engineering, with several years of experience directly managing technical teams. Expertise in distributed systems architecture, application-layer API design (including REST and GraphQL), and event-driven processing is standard. Proficiency in programming languages like Java, Scala, or Python, along with hands-on experience with data processing frameworks (e.g., Spark, Flink), messaging queues (e.g., Kafka), and various datastores (SQL, NoSQL, caches) is highly valued. Beyond technical skills, successful candidates demonstrate an ability to balance long-term platform health with urgent product needs, exhibit exceptional communication and stakeholder influence skills, and have a proven commitment to operational excellence, system reliability, and security. This career is ideal for those passionate about leveraging data engineering to create exceptional support experiences and drive business impact through internal platform development.
