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Metro Bangalore, common data stack, moderate global brand, and senior managerial level yield medium competition.
Low — core data engineering and leadership skills are highly transferable across industries.
High due to explicit 8+ years, 3+ years leading teams, and mandatory modern data platform skills.
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Lead and grow a high-performing Data Engineering team building scalable, reliable, cloud-based data platforms and pipelines supporting business intelligence, analytics, machine learning, and operational reporting.
Drive technical excellence and modern data engineering best practices, including design reviews, data quality, governance, and platform scalability and reliability.
Own end-to-end delivery of data engineering initiatives, balancing business priorities with long-term maintainability, collaborating cross-functionally with Product, Analytics, Data Science, Platform Engineering, DevOps, and Architecture.
8+ years of software or data engineering experience, with 3+ years leading Data Engineering teams.
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field.
Strong hands-on experience with scalable data platforms, distributed processing systems, ETL/ELT pipelines, and data technologies like Python, SQL, Spark, Kafka, Airflow, Databricks, Snowflake.
Experience with cloud platforms (AWS, Azure, or GCP), data lake and warehouse architectures, data governance, CI/CD pipelines, Infrastructure as Code, DevOps, and Agile methodologies.
Experienced in managing and scaling technical teams focused on cloud-native, distributed data platforms with emphasis on real-time and batch data pipelines.
Skilled at balancing immediate business needs with long-term platform scalability, maintainability, and technical debt reduction in a complex, cross-functional environment.
Strong communicator who effectively manages stakeholder expectations, mentors engineers, and drives operational excellence in globally distributed or large-scale teams.