Senior Java Engineer (Iceberg + QuickSight)

Project Description:

We are looking for a Senior Java Engineer to join our Capital Markets technology team and contribute to building a modern analytics platform. This role combines strong backend engineering expertise with hands-on work across the data layer and visualization layer, leveraging technologies such as Apache Iceberg and Amazon QuickSight.

You will work on client-server analytics applications, primarily on the backend, while playing a key role in enabling scalable data solutions and delivering business insights.

Responsibilities:

Key Responsibilities

Design and develop robust Java-based backend services for analytics applications

Build and optimize data pipelines and storage solutions using Apache Iceberg

Work with large-scale financial datasets, ensuring performance and scalability

Enable reporting and insights through integration with Amazon QuickSight

Develop APIs supporting both real-time and batch analytics workflows

Collaborate closely with data engineers, analysts, and business stakeholders

Contribute to architecture decisions, system design, and best engineering practices

Mandatory Skills Description:

7–10+ years of experience in Java development (Java 8+)

Strong experience with Spring / Spring Boot ecosystem for building enterprise applications

Experience designing and building RESTful APIs and microservices architectures

Solid understanding of containerization and deployment (Docker; Kubernetes is a plus)

Experience with distributed systems, multithreading, and performance optimization

Hands-on experience with data lake technologies (Apache Iceberg preferred)

Strong proficiency in SQL, data modeling, and large-scale data processing

Experience with AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, etc.) and integrating them into daily workflows

Familiarity with Agentic SDLC concepts (AI-driven development workflows, automation of coding/testing/review processes)

Experience working with capital markets systems (trading, risk, or analytics)

Strong understanding of software engineering best practices (CI/CD, testing frameworks, code quality, version control)

Nice-to-Have Skills Description:

Knowledge of AWS ecosystem (S3, Athena, Glue, QuickSight, IAM, etc.)

Experience with Kafka, Spark, or other data processing frameworks

Familiarity with microservices and cloud-native architecture patterns

Knowledge of financial instruments and analytics workflows