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 define product capabilities and deliver measurable business outcomes.

- Drive continuous improvement in engineering practices, team capabilities, and product delivery.

Qualifications:

- 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.

Preferred Qualifications:

- Experience developing enterprise data products, shared data platforms, reusable data services, or self-service data capabilities.

- Experience with Apache Spark, Databricks, Snowflake, 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.