1.JD Product Engineering Manager Data Products & Data Platforms About the Role We are looking for a Product Engineering Manager with a strong background in Data Product Engineering to lead the design, development, deployment, and evolution of enterprise-scale data products and platforms. The ideal candidate combines hands-on data engineering expertise with product lifecycle ownership and engineering leadership, along with experience in full-stack development, including React. Key Responsibilities • • • • • • • • • • Own the end-to-end lifecycle of data products, from product requirements and architecture to development, deployment, production support, and continuous enhancement. Lead the engineering of scalable data products, data platforms, pipelines, APIs, and reusable data services. Translate business and product requirements into technical architectures, engineering roadmaps, and scalable solutions. Drive full-stack development for data products, including React-based user interfaces and backend services/API integrations. Establish engineering standards for architecture, code quality, CI/CD, automated testing, deployment, data quality, monitoring, and observability. Ensure data products meet requirements for scalability, reliability, performance, maintainability, and enterprise integration. Manage engineering delivery, including planning, prioritization, resource allocation, timelines, risks, and quality. Lead, mentor, and develop a team of 10+ engineers across data engineering, frontend, backend, and platform engineering. Collaborate with Product Management, architects, data engineers, data scientists, and customers to dene product capabilities and deliver measurable business outcomes. Drive continuous improvement in engineering practices, team capabilities, and product delivery. Qualications • • • 12+ years of experience in Data Product Engineering, Data Platform Engineering, or related data-intensive product development. Demonstrated experience building and owning enterprise-scale data products throughout their lifecycle. Strong foundation in data engineering, including data pipelines, integration, distributed processing, data modelling, and modern data architectures. Experience leading and mentoring engineering teams of 10+ members. Hands-on experience with React and integrating frontend applications with backend APIs and data services. • • • Strong understanding of distributed systems, databases, cloud platforms, and software engineering principles. • • Experience with AWS, Azure, or GCP. Practical knowledge of CI/CD, automated testing, deployment automation, production support, and observability. • Proven ability to make technical architecture decisions while balancing product priorities, technical debt, quality, and delivery. 2 Preferred Qualications • Experience developing enterprise data products, shared data platforms, reusable data services, or self-service data capabilities. • • Experience with Apache Spark, Databricks, Snowake, or similar data technologies. Familiarity with React component architecture, API-driven applications, and frontend/backend integration. • • Experience with data orchestration, pipeline monitoring, data quality, and data observability. Exposure to cloud-native architectures, infrastructure automation, and Agile product delivery. • Experience with Hadoop ecosystem technologies such as HDFS, MapReduce, Oozie, Hive, Impala, Spark, Kerberos, or Kafka. 3