Senior Java Architect - Data Lakes & Microservices

Citi; Jersey City (NJ); Híbrido; USD 142.000 - 213.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

Competitive benefits

Wellbeing program

Descripción de la vacante

Citi is seeking a Senior Data Architect to lead the design, development, and implementation of enterprise-grade Java applications and data platforms in Jersey City, NJ. You will mentor teams, define roadmaps, and drive best practices across cloud-native and hybrid-cloud environments.

Responsibilities include building scalable microservices with Java/Spring, designing data pipelines with Kafka, Flink, and Iceberg, and optimizing storage across Delta Lake, Iceberg, Snowflake, and Databricks.

Formación

• Expert-level proficiency in Java, including Spring Boot and Spring Cloud.
• Experience building scalable data lakes and microservices.
• Deep data framework knowledge: Iceberg, Pinot, Hive/HDFS.
• Real-time streaming with Kafka/Flink in production.
• SQL proficiency across AWS/Azure/GCP; containers with Docker/Kubernetes.

Responsabilidades

• Define and evolve architecture for Citi's data lake and analytics platform.
• Design and develop high-performance Java microservices.
• Lead and mentor data engineers; promote best practices.
• Build data pipelines using Kafka, Flink, Iceberg, and Pinot.
• Optimize storage and query performance across lakehouses.

Conocimientos

Java

Spring framework

Microservices

Kafka

Flink

Iceberg

Pinot

Cloud platforms

Educación

BS/MS in CS/Engineering

Herramientas

Docker

Kubernetes

Databricks

Snowflake

Descripción del empleo

Citi is seeking a Senior Data Architect to lead the design, development, and implementation of enterprise-grade Java applications and data platforms in Jersey City, NJ. You will mentor teams, define roadmaps, and drive best practices across cloud-native and hybrid-cloud environments.

Responsibilities include building scalable microservices with Java/Spring, designing data pipelines with Kafka, Flink, and Iceberg, and optimizing storage across Delta Lake, Iceberg, Snowflake, and Databricks.