Lead Java Developer - VP at Citi

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Mar, 26

Salary

0.0

Posted On

17 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Java, Apache Kafka, Apache Flink, Apache Pinot, Distributed Systems, Data Storage, SQL, Agile, Cloud-Native, Kubernetes, API Development, Performance Engineering, Financial Domain Knowledge, Data Governance, Technical Leadership, Mentoring

Industry

Financial Services

Description

Architectural Leadership: Serve as the principal architect for scalable, high-performance Java-based real-time data solutions, ensuring robust design for high availability, fault tolerance, and resilience for both real-time and EOD risk processes. Strategic Implementation: Drive the strategic implementation and optimization of distributed stream processing frameworks (Apache Kafka, Apache Flink) and real-time data storage technologies (Apache Pinot) for ultra-low-latency analytics and complex event processing. Data Pipeline Mastery: Lead the end-to-end design, development, and operation of real-time streaming data pipelines, integrating with large-scale object storage solutions like S3 and analytics engines such as Trino. Technical Excellence & Mentorship: Champion continuous improvement in data reliability, efficiency, and scalability. Establish and enforce best practices for code quality, performance optimization, and system resilience through hands-on leadership and thorough peer code reviews. Mentor and technically guide senior and lead developers. SDLC Ownership: Drive significant contributions across all phases of the Agile software development lifecycle, from architectural vision and detailed design to implementation, deployment, monitoring, and ongoing support for critical real-time data systems. Cross-Functional Collaboration: Collaborate strategically with business analysts, product managers, quality assurance teams, and other engineering leads to ensure the delivery of seamlessly integrated, high-impact technology solutions that align with business objectives and architectural standards. Innovation & Research: Stay abreast of industry trends and emerging technologies in real-time data processing, distributed systems, and cloud-native architectures, evaluating and proposing their adoption where beneficial. Senior Data Engineering Expertise: 7+ years of progressive experience in data engineering and software development, with a significant focus on building high-performance, large-scale distributed systems. Java Mastery: Expert-level command of Java (version 11 or higher) with a deep understanding of concurrent programming, multithreading, advanced OOP concepts, design patterns, and performance tuning. Apache Kafka or related technologies: For high-throughput, fault-tolerant message queuing and streaming. Apache Flink or related technologies: For advanced real-time stream processing, complex event processing (CEP), and stateful computations. Apache Pinot or related technologies: For ultra-low-latency OLAP queries on streaming data. Distributed Systems Architecture: Strong expertise in designing and implementing highly available, scalable, and resilient distributed systems. Data Storage & Querying: Extensive experience with large-scale data storage solutions (e.g., S3, HDFS) and distributed query engines (e.g., Trino/Presto, Spark SQL). SQL Proficiency: Advanced SQL knowledge with experience in optimizing complex queries for large datasets. Agile Leadership: Demonstrated experience leading technical initiatives and teams within an Agile software development environment. Communication & Problem Solving: Exceptional communication, analytical, and problem-solving skills, with the ability to articulate complex technical concepts to diverse audiences and drive consensus on architectural decisions. Cloud-Native Data Ecosystems: Experience with cloud-native data services on platforms like AWS, Azure, or GCP, particularly related to streaming and real-time analytics. Container Orchestration: Hands-on experience with containerization and orchestration technologies such as Kubernetes and OpenShift for deploying and managing real-time data applications. API Development: Experience in designing and implementing high-performance RESTful APIs and event-driven microservices architectures. Performance Engineering: Deep understanding and experience with performance tuning, profiling, and optimization of real-time streaming applications and data stores. Financial Domain Knowledge: Strong understanding of financial derivatives, fixed income products, and risk management concepts from a technical data perspective. Technical Leadership & Mentoring: A proven track record of mentoring junior to senior engineers, fostering technical growth, and building high-performing engineering teams. Data Governance & Security: Experience with data governance, data quality, and security best practices in real-time data environments. Global Team Experience: Experience working effectively within a geographically distributed, global development team. Advanced Degree: Strong academic record, ideally with a Master's or Ph.D. in Computer Science, Electrical Engineering, or a related technical/quantitative discipline. Architectural Delivery: Demonstrable success in architecting, leading, and delivering complex, multi-tiered, high-performance real-time data applications and platforms. Domain Acumen: A deep understanding of financial derivatives (with fixed income products) and risk analytics, or a strong demonstrable capability and eagerness to rapidly acquire expertise in this domain from a technical perspective. ------------------------------------------------------ For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------ Anticipated Posting Close Date: Dec 23, 2025 ------------------------------------------------------

Responsibilities

Serve as the principal architect for scalable, high-performance Java-based real-time data solutions and drive the strategic implementation of distributed stream processing frameworks. Lead the design, development, and operation of real-time streaming data pipelines while collaborating with cross-functional teams.