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Tier-1 bank brand, generalist data-engineer title, and metro hiring increase competition.
Data engineering skills transfer across industries but require tooling and domain knowledge, so medium sensitivity.
No explicit years or certifications but requires hands-on data engineering, giving moderately strict filters.
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Design and deliver secure, stable, scalable software products supporting business objectives in an agile team.
Develop, troubleshoot, and maintain high-quality production code and algorithms, ensuring design constraints are met.
Analyze and visualize large data sets to identify issues and improve code quality and system architecture, leveraging AI-assisted development tools responsibly.
Practical experience in system design, application development, testing, and operational stability.
Proficiency in one or more programming languages and database querying languages in a large corporate environment.
Knowledge of Software Development Life Cycle and agile methodologies including CI/CD, application resiliency, and security.
Hands-on experience using enterprise-authorized AI-assisted software development tools with ability to validate and refine AI-generated outputs.
Experienced in delivering complex software solutions within large, agile corporate environments with emphasis on secure, resilient architectures.
Skilled at integrating AI-assisted development tools responsibly into engineering workflows and guiding peers.
Analytical approach to identifying hidden problems through data insights and improving system architecture and coding hygiene.