Java Engineer - Data Specialization

NTT DATA•Nashville, TN

60d

About The Position

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.We are currently seeking a Data Engineer to join our team in Nashville, Tennessee (US-TN), United States (US) Job Description: Data Engineer POSITION OVERVIEW We are seeking a talented and motivated Data Engineer to join our innovative data engineering team supporting a leading financial services client. This position focuses on designing and implementing data solutions that enable business decision optimization, investment portfolio analysis, and risk and compliance management. The ideal candidate will have 4+ years of experience in software and data engineering, with strong proficiency in Kafka, PL/SQL, and Java for building and managing complex data pipelines. KEY RESPONSIBILITIES Data Engineering: Design, develop, and maintain scalable data pipelines leveraging Kafka, PL/SQL, and Java. Build and manage multiple data pipelines to source, transform, and deliver data based on business requirements Data Movement and Transformation: Implement reliable and efficient data workflows using Kafka and PL/SQL for data movement, streaming, and transformation. Utilize Java as a primary programming language for data pipeline and integration development. Client Collaboration: Work closely with financial services clients to design and implement modern data solutions that enhance business decision-making and operational efficiency. Manage multiple stakeholder groups and their evolving requirements. Industry Leadership: Partner with a client that is at the forefront of leveraging data to drive innovation in investment management and business optimization. Innovation and Learning: Proactively learn and adopt new technologies by applying current skills and expanding technical expertise. Continuously identify opportunities for improvement and integrate emerging technologies within tight learning cycles. Team Collaboration: Collaborate within a Pod of 4+ data engineers, working toward shared goals in a consultative, client-facing environment.

Requirements

Advanced proficiency in Java programming for data engineering.
Strong experience in Kafka-based streaming and data movement.
Expertise in PL/SQL and relational database systems.
Hands-on experience in data integration and RESTful API development.
Strong understanding of data warehousing and real-time data processing.
Familiarity with GitHub for version control and Jenkins for CI/CD pipelines.
Technical problem-solving and analytical thinking.
Collaboration and cross-functional teamwork.
Client engagement and communication skills.
Innovation and continuous learning.
Adaptability in dynamic technical environments.
Bachelor's degree in Computer Science, Engineering, or a related field.
4+ years of experience in software and data engineering.
Proven ability to build and manage data pipelines using Kafka, PL/SQL, and Java.

Nice To Haves

Experience with financial data analysis, risk, and compliance data management.
Strong understanding of real-time data warehousing and integration.
Demonstrated ability to quickly learn and apply new technologies.

Responsibilities

Design, develop, and maintain scalable data pipelines leveraging Kafka, PL/SQL, and Java.
Build and manage multiple data pipelines to source, transform, and deliver data based on business requirements
Implement reliable and efficient data workflows using Kafka and PL/SQL for data movement, streaming, and transformation.
Utilize Java as a primary programming language for data pipeline and integration development.
Work closely with financial services clients to design and implement modern data solutions that enhance business decision-making and operational efficiency.
Manage multiple stakeholder groups and their evolving requirements.
Partner with a client that is at the forefront of leveraging data to drive innovation in investment management and business optimization.
Proactively learn and adopt new technologies by applying current skills and expanding technical expertise.
Continuously identify opportunities for improvement and integrate emerging technologies within tight learning cycles.
Collaborate within a Pod of 4+ data engineers, working toward shared goals in a consultative, client-facing environment.