Principal Data Engineer

Job Description Principal Data EngineerLocation: Johnston, RIRole OverviewPrincipal-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 ResponsibilitiesArchitect, design, and implement enterprise-grade Java-based data platforms and distributed processing systemsBuild and maintain production-ready Spark applications (Java) for batch and real-time processingDesign and evolve Kafka-based event streaming and ingestion pipelinesDevelop and consume REST APIs within microservices architectureLead architecture ensuring scalability, reliability, and regulatory complianceApply strong object-oriented design and engineering practicesMentor engineers on performance tuning and production readinessDesign and implement MDM solutions (match, merge, survivorship logic)Ensure data quality, observability, and system stabilitySupport production deployments and operational handoffsRequired Skills & Experience10 12+ years experience in Java/backend or data engineeringHands-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 experienceStrong Microservices experience (Spring Boot, REST APIs)Experience in API development and API consumptionHands-on Spark experience (batch and streaming)Strong SQL and data modeling skillsAWS experience (S3, Glue, EMR, Redshift)Experience in regulated/data governance environmentsCI/CD, Git, Docker/Kubernetes familiarityPreferredScala or Python experienceTalend/DataStage exposureData lake experience (Iceberg/Parquet)Frontend/API integration exposureExperience supporting large-scale production systemsMandatory Screening CriteriaCandidates 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.