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Tier-1 brand, generalist fullstack title, metro location, and broad skillset increase candidate competition.
Core fullstack engineering skills are broadly transferable across industries despite data-platform context.
No explicit years or certifications and skills are framed as desirable and learnable, so filters are flexible.
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Develop and deploy cloud-hosted, scalable distributed systems managing structured and unstructured data for Elsevier's Submission and Editorial platform.
Collaborate with business stakeholders and cross-functional teams to define requirements, inform architecture decisions, and improve delivery processes in an Agile and DevOps environment.
Build data infrastructure components supporting data discovery, analytics, operational reporting, and AI-driven insights using AWS, microservices, and modern data engineering practices.
Proficiency in Java, JavaScript/TypeScript frameworks (React, Angular, Spring Boot), and SQL with willingness to learn additional technologies.
Experience with version control systems (Git) and modern IDEs (IntelliJ or VS Code).
Some experience with test-driven development and mocking libraries (Jest, Mockito).
Work Experience Required: Not explicitly mentioned in the JD; location-based requirement: candidate must be within commutable distance of Chennai office for hybrid work.
Experienced in building and maintaining cloud-native, event-driven, microservice distributed data systems, preferably involving data lakes and data mesh architectures.
Comfortable working in Agile/Scrum and DevOps settings, collaborating with multiple stakeholders in a fast-paced environment.
Open to continuous learning, especially in data modelling, PySpark, distributed data processing frameworks, and modern data catalog/integration tools like Collibra, Snowflake, and Databricks.