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Tier-1 brand, popular big-data role, metro hiring and broad skill requirements drive high competition.
Core big-data engineering skills transfer across industries, though financial domain experience is beneficial.
Explicit 8–10 years requirement plus mandatory big-data tech stack enforces strict shortlisting.
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Lead design, implementation, and optimization of scalable big data architecture and pipelines handling large-scale data processing.
Oversee deployment and management of big data frameworks like Hadoop, Spark, Kafka, ensuring high availability, resilience, and performance.
Provide technical leadership and mentorship to development teams while integrating emerging technologies and ensuring data governance and security compliance.
8-10 years of software development experience, primarily with large-scale data ingestion, persistence, and retrieval.
Bachelor's or Master’s degree in Computer Science, Information Technology, or related field.
Proficiency with big data technologies including Hadoop, Spark, Kafka, Flink, and NoSQL databases; strong programming skills in Java, Scala, or Python.
Experience designing data transformation processes using Spark (Scala, PySpark) and building data pipelines that ensure data quality and integrity.
Experienced in leading large-scale big data engineering projects with a focus on architecture strategy and performance optimization.
Strong technical leadership background with the ability to mentor teams and manage multiple projects in high-impact environments.
Domain familiarity with financial services/core banking systems and transitioning ETL frameworks to modern big data tools such as Apache Spark.