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Strong corporate brand, remote/hybrid flexibility, generalist Data Engineer title, and broad stack increase applicant competition.
Cobol/mainframe and data engineering specialization reduce cross-industry transferability.
Explicit 1–3 years requirement plus mandatory data engineering and legacy Cobol/mainframe skills raise filtering strictness.
Design, develop, test, and support business-critical enterprise applications using legacy (Cobol/Mainframe) and modern (Python, Java, Angular, Spring Boot) technologies in an agile environment.
Develop and maintain scalable data pipelines for streaming and batch data including API integrations, ensuring data quality, security, and operational stability.
Diagnose and resolve complex production and integration issues, contribute to release and operational readiness, and apply AI-assisted and secure software engineering practices.
Experience: 1-3 years relevant experience in data quality, data management, or data engineering fields.
Technical skills: Experience with Cobol/Mainframe legacy systems, Python, Java, Angular, Spring Boot, APIs, microservices, CI/CD pipelines, monitoring tools, and vulnerability management.
Education: Undergraduate degree in Computer Science, MIS, Business, Statistics, Math, or related field strongly preferred; Graduate studies are a plus.
Work location: India-based, aligned to Indian business hours with hybrid/remote/in-office model; experience working in agile and global collaboration environments.
Candidate proficient in end-to-end software development lifecycle including design, development, testing, deployment, and maintenance within complex integrated environments.
Capability to translate business data requirements into technical solutions with attention to secure software practices and alignment with engineering and risk standards.
Experience working effectively in fast-paced operational settings, leveraging AI-enabled tools for development, troubleshooting, automation, and security improvements.