Principal Data Engineer

Job Description Principal Data Engineer

Location: Johnston RI

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

Mandatory Screening Criteria
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.