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Mid-level Bangalore data-platform role with niche Scala/Spark/knowledge-graph requirements reduces generalist competition.
Highly domain-specific data engineering and knowledge-graph skills limit cross-industry transferability.
Mandatory 5+ years and specialized Spark, Scala, Kafka, Databricks, and knowledge-graph experience.
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Lead design and implementation of scalable data processing workflows and microservices using Spark, Kafka Streams, and Kubernetes for Elsevier's data platform.
Develop and maintain reusable platform components and data products enabling scalable, reliable, and high-performance data solutions.
Mentor junior engineers and actively participate in architecture discussions and continuous improvement initiatives within data engineering architecture.
5+ years of professional experience in software or data engineering focused on large-scale batch and streaming data systems.
Hands-on experience with Spark, Kafka, Databricks, Scala; Java knowledge is a plus.
Proficiency in AWS services (S3, EMR, Athena/Glue, SQS) and Infrastructure as Code tools.
Strong understanding of distributed systems, semantic technologies (RDF, data modeling), and experience with graph-based data models or knowledge graph architectures.
Experienced in building reusable engineering platforms and implementing observability best practices (logging, monitoring, metrics, alerting).
Capable of independently driving technical solutions end-to-end in Agile environments and participating in cross-team collaborations.
Strong background in engineering large-scale, distributed data systems with expertise in functional programming and software engineering best practices using Scala.