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Tier-1 brand, metro location, and mid-level Big Data role increase candidate competition.
Big Data engineering skills transfer across industries, but financial domain expectations moderately increase specificity.
Multiple mandatory technical skills and explicit 5+ years requirement create strict shortlisting.
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Manage data integration and analysis of disparate systems using big data technologies and build scalable data acquisition and integration solutions.
Drive adoption of AI-assisted software engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing validation standards and promoting reuse across the team.
Interface with cross-functional and remote teams to build extensible, consumable products and provide complex data analysis for business intelligence integration designs.
5+ years of applied software engineering experience with formal training or certification.
Hands-on experience with Spark, Scala, Kafka streaming applications, and big data integration projects.
Experience leading use of approved AI-assisted software development tools and strong understanding of responsible AI use in engineering workflows.
Strong CICD experience (Jenkins, Git, Artifactory, Yaml, Maven), Java ecosystem (Spring Boot, JUnits, API, Swagger), and knowledge of big data querying (Pig, Hive, Impala) plus data storage formats (Parquet, ORC, Avro).
Proven leader experienced in managing teams of 5-10 engineers and driving adoption of advanced AI-assisted engineering tools and practices.
Strong background in building scalable, high-performance distributed systems with data warehousing and data lake expertise.
Comfortable working in cross-functional and geographically distributed teams, with capability to quickly learn new technologies in dynamic environments.