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Tier-1 brand plus metro location but specialized platform skillset yields medium competition.
Role requires specialized large-scale data platform experience, so background transferability is high sensitivity.
Explicit years, management expectation, and deep platform tech stack make shortlisting highly strict.
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Lead and grow a team responsible for building and scaling a high-throughput batch processing data platform powering AI products and critical business decisions.
Design and evolve a scalable, AI-native batch processing platform using Apache Spark, workflow orchestration tools, and Kubernetes, processing petabyte-scale data reliably and cost-efficiently.
Drive platform governance, security, reliability, and cross-team collaboration while ensuring high-quality roadmap execution and observability.
8–12+ years in software or data engineering.
3–5+ years managing engineering teams, especially platform or large-scale data systems teams.
Strong hands-on experience with distributed data processing frameworks (e.g., Spark, Flink) and AWS preferred.
Deep understanding of data lakehouse architectures, data modeling, and workflow orchestration tools (e.g., Airflow, Temporal).
Experienced leader capable of managing and scaling high-performing engineering teams focused on data platform and batch processing.
Technical proficiency in designing and operating large-scale distributed data systems with a focus on reliability, scalability, and AI integration.
Comfortable working cross-functionally with stakeholders, driving execution, and influencing architecture decisions at the organizational level.