Role Overview
Principal-level Java engineer to design and build enterprise-grade, real-time and batch data processing systems using Java, Spark, Kafka, and Microservices architecture. Strong focus on event-driven pipelines, API development (build + consume), and high-volume streaming platforms.
Key Responsibilities
* Architect, design, and implement enterprise-grade Java-based data platforms and distributed processing systems
* Build and maintain production-ready Spark applications (Java) for batch and real-time processing
* Design and evolve Kafka-based event streaming and ingestion pipelines
* Develop and consume REST APIs within microservices architecture
* Lead architecture ensuring scalability, reliability, and regulatory compliance
* Apply strong object-oriented design and engineering practices
* Mentor engineers on performance tuning and production readiness
* Design and implement MDM solutions (match, merge, survivorship logic)
* Ensure data quality, observability, and system stability
* Support production deployments and operational handoffs
Required Skills & Experience :
* 10 12+ years experience in Java/backend or data engineering
* Hands-on experience building real-time data pipelines (Kafka, Spark Streaming/Flink)
* Solid knowledge of relational databases (Redshift, PostgreSQL, Snowflake) and NoSQL databases (MongoDB or similar)
* Strong Kafka and event-driven architecture experience
* Strong Microservices experience (Spring Boot, REST APIs)
* Experience in API development and API consumption
* Hands-on Spark experience (batch and streaming)
* Strong SQL and data modeling skills
* AWS experience (S3, Glue, EMR, Redshift)
* Experience in regulated/data governance environments
* CI/CD, Git, Docker/Kubernetes familiarity
Preferred
* Scala or Python experience
* Talend/DataStage exposure
* Data lake experience (Iceberg/Parquet)
* Frontend/API integration exposure
* Experience supporting large-scale production systems
Candidates must have hands-on experience building real-time/event-driven data pipelines using Kafka and Spark/Flink, along with strong microservices and API development experience.
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