Software Engineer II - Java, UI, Spark, Kafka

📍 Job Overview

Job Title: Software Engineer II - Java, UI, Spark, Kafka

Company: JPMorgan Chase & Co.

Location: Mumbai, Maharashtra, India

Job Type: Full time

Category: Software Engineering / Full-Stack Development

Date Posted: 2026-08-04T08:02:00

Experience Level: 2-5 Years

Remote Status: On-site

🚀 Role Summary

• Design, build, and maintain end-to-end full-stack solutions utilizing Java technologies and modern UI frameworks within an agile development environment.

• Develop and support robust backend services and APIs, ensuring secure and efficient data handling, authentication, and integration patterns.

• Leverage enterprise-authorized AI coding assist tools to enhance code quality, accelerate delivery, and improve productivity throughout the Software Development Life Cycle (SDLC).

• Integrate with data platforms, data services, and event streams to expose reliable datasets and metrics, supporting data-centric use cases and improving data governance.

📝 Enhancement Note: This role, while titled "Software Engineer II," is deeply embedded within the financial services sector, specifically the Commercial & Investment Bank division. The emphasis on full-stack delivery, integration with data platforms, and the specific mention of Kafka and Spark indicate a need for engineers capable of building complex, data-intensive applications. The inclusion of AI-assisted development tools is a significant modern aspect, requiring candidates to be adaptable and proficient in leveraging these technologies responsibly.

📈 Primary Responsibilities

• Design, develop, and maintain scalable, secure, and reliable end-to-end full-stack solutions using Java and modern UI frameworks.

• Build and support backend services and APIs (REST/GraphQL), implementing robust authentication, authorization, and error handling mechanisms.

• Utilize enterprise-authorized AI coding assist tools for code generation, refactoring, unit test creation, and documentation, ensuring validation through peer review and automated testing.

• Develop responsive, accessible, and reusable frontend experiences and UI components in alignment with engineering standards.

• Integrate with data platforms, data services, and event streams (e.g., Kafka) to expose reliable datasets and metrics for upstream and downstream consumers.

• Collaborate with data horizontal teams to enhance data quality, observability, lineage, and governance for applications.

• Apply strong engineering practices including code reviews, comprehensive unit and integration testing, CI/CD pipelines, performance tuning, and proactive production support.

• Contribute to system design discussions, focusing on driving non-functional requirements such as security, resiliency, scalability, and low latency.

• Troubleshoot and resolve complex production issues across UI, services, and data interactions, implementing sustainable fixes and automation strategies.

📝 Enhancement Note: The responsibilities highlight a comprehensive full-stack role with a strong emphasis on data integration and engineering best practices. The explicit mention of AI coding assist tools suggests a forward-thinking development approach, requiring candidates to demonstrate adaptability and a critical eye for AI-generated outputs. The need to troubleshoot production issues across multiple layers (UI, services, data) points to a demand for well-rounded engineers with a deep understanding of system architecture and interdependencies.

🎓 Skills & Qualifications

Education: Formal training or certification on software engineering concepts.

Experience: 2+ years of applied software engineering experience with significant full-stack delivery.

Required Skills:

• Strong proficiency in <strong>Java</strong> and enterprise backend development, with experience in frameworks like <strong>Spring / Spring Boot</strong>.

• Experience building <strong>microservices</strong> and well-designed APIs (REST).

• Solid understanding of <strong>frontend development</strong> with at least one modern framework (e.g., <strong>React, Angular, or Vue</strong>), including HTML/CSS/TypeScript/JavaScript.

• Strong knowledge of <strong>relational databases</strong> and SQL (e.g., PostgreSQL/Oracle), including schema design and query optimization.

• Hands-on experience with engineering practices such as <strong>CI/CD pipelines</strong>, automated testing, and source control (<strong>Git</strong>).

• Experience using enterprise-authorized AI-assisted software development tools (e.g., for coding, testing, troubleshooting, documentation) with the ability to critically evaluate AI-generated outputs.

• Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security expectations.

• Practical knowledge of <strong>security</strong> concepts (OAuth2/JWT, secure coding, secrets management) and production readiness.

• Strong problem-solving skills, ability to work effectively across teams, and excellent written/verbal communication skills. Preferred Skills:

• Experience with <strong>data engineering and data platform integrations</strong>, including:

  • Messaging/streaming: <strong>Kafka</strong> (or equivalent)
  • Data processing: <strong>Spark</strong> (or equivalent)
  • Data warehousing/lakes: Snowflake/Databricks/Hive (or similar)
  • Orchestration: <strong>Airflow</strong> (or similar)

• Familiarity with <strong>data governance</strong> concepts: metadata/lineage, data quality checks, access controls, and auditability.

• Experience with observability tooling: centralized logging, metrics, tracing (e.g., OpenTelemetry concepts).

• Containerization and orchestration: <strong>Docker</strong> and Kubernetes (or equivalent).

• Domain exposure to loan origination/servicing systems or regulated financial workflows.

📝 Enhancement Note: The "Required Skills" section clearly outlines the foundational technical competencies. The "Preferred Skills" section is particularly important for this role, as it directly points to experience with big data technologies (Kafka, Spark) and data engineering practices, which are crucial for building sophisticated financial applications. Candidates with experience in these areas will have a significant advantage. The mention of AI-assisted tools is a key differentiator that should be highlighted by applicants.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of end-to-end full-stack application development, showcasing integration of backend Java services with modern frontend frameworks.

• Case studies detailing the design and implementation of microservices and APIs, highlighting considerations for scalability, security, and performance.

• Examples of contributions to CI/CD pipelines, automated testing frameworks, and Git-based workflows, emphasizing efficiency and reliability.

• Proof of work involving data integration, demonstrating the ability to connect with data platforms, expose datasets, and support data-centric use cases.

• Documentation or examples showcasing the application of enterprise-authorized AI coding assist tools, including validation of AI-generated outputs. Process Documentation:

• Workflow design and optimization: Showcase how you have analyzed, documented, and improved development workflows or SDLC processes.

• Implementation and automation: Provide examples of implemented automation strategies for testing, deployment, or operational tasks.

• Measurement and performance analysis: Demonstrate how you have used metrics to measure the effectiveness of processes and identify areas for improvement.

📝 Enhancement Note: For a role at JPMorgan Chase, especially involving complex financial systems, a strong portfolio is critical. It should not just list projects but articulate the candidate's thought process, problem-solving approach, and the impact of their contributions. For this specific role, demonstrating experience with data integration (Kafka, Spark) and the responsible use of AI tools within development workflows will be key differentiators. Candidates should be prepared to walk through their portfolio, explaining the technical challenges, their solutions, and the quantifiable outcomes.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for Software Engineer II roles with 2-5 years of experience in Mumbai, India, the estimated annual salary range is ₹12,00,000 to ₹25,00,000. This range accounts for the specific technical requirements (Java, UI, Spark, Kafka) and the prestigious nature of employment at JPMorgan Chase.

Benefits:

• Comprehensive health insurance coverage (medical, dental, vision) for employees and dependents.

• Retirement savings plans, including provident fund contributions and investment options.

• Paid time off, including vacation days, sick leave, and public holidays.

• Opportunities for professional development, including training programs, certifications, and workshops.

• Employee assistance programs offering confidential counseling and support services.

• Potential for performance-based bonuses and stock options.

• Access to firm-wide employee discounts and wellness programs.

Working Hours: 40 hours per week, with standard business hours generally aligning with Mumbai's local time (Asia/Kolkata). Flexibility may be required for critical production support or project deadlines.

📝 Enhancement Note: The salary range provided is an estimate based on current market data for similar roles in Mumbai, India, considering the experience level and the specific technical stack requested. JPMorgan Chase is a top-tier financial institution, and compensation packages typically reflect this, often including robust benefits and potential for bonuses. The explicit mention of AI tools in the job description might also position this role towards the higher end of the salary band due to the specialized nature of the skills.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (Commercial & Investment Banking). JPMorgan Chase operates within a highly regulated and dynamic financial market, demanding precision, security, and innovation in its technology solutions.

Company Size: Over 10,000 employees globally. This large, established organization offers significant career pathways and resources, while its divisional structure (Commercial & Investment Bank) provides specialized environments.

Founded: 2000 (through the merger of Chase Manhattan and J.P. Morgan & Co.), with roots tracing back to 1799. This long history signifies stability, deep industry knowledge, and a strong commitment to long-term growth and technological advancement.

Team Structure:

• The role is within an agile team focused on enhancing, designing, and delivering software components for the Commercial & Investment Bank.

• This team likely comprises Software Engineers, QA Engineers, Product Owners, and potentially Business Analysts, working collaboratively.

• Reporting structure will likely involve a Tech Lead or Engineering Manager, with potential for cross-functional collaboration with data specialists, architects, and security teams. Methodology:

• Agile development methodologies are standard, emphasizing iterative development, continuous feedback, and adaptability.

• Emphasis on robust engineering practices: code reviews, unit/integration testing, CI/CD, performance tuning, and proactive production support.

• Data-driven decision-making, integrating with data platforms and leveraging analytics for insights and improvements.

• Secure coding standards and adherence to strict financial industry regulations.

Company Website: https://www.jpmorganchase.com/

📝 Enhancement Note: JPMorgan Chase's culture is deeply rooted in financial services, prioritizing security, compliance, and reliability. For operations professionals, this translates to a structured, process-oriented environment where data integrity and robust solutions are paramount. The mention of "Commercial & Investment Bank" suggests a fast-paced, high-stakes environment where technology directly impacts critical business functions and client services.

📈 Career & Growth Analysis

Operations Career Level: Software Engineer II represents an intermediate level, typically requiring 2-5 years of experience. This role is for an individual contributor focused on technical execution, system design contributions, and problem-solving. It’s a stepping stone to more senior engineering roles.

Reporting Structure: The role reports to a Tech Lead or Engineering Manager within an agile team. Collaboration will be extensive with peers, architects, product owners, and potentially stakeholders from other business units or data teams.

Operations Impact: While not a traditional "Revenue Operations" role, this Software Engineer II position has a significant impact on revenue by building and maintaining the technology infrastructure that supports critical commercial and investment banking operations. Enhancements in system performance, security, and new feature delivery directly influence operational efficiency, client satisfaction, and the firm's ability to execute financial transactions and services.

Growth Opportunities:

• Technical Specialization: Deepen expertise in Java, specific Spring modules, modern UI frameworks, or data technologies like Kafka and Spark.

• Architectural Acumen: Progress towards Senior Software Engineer or Architect roles by contributing to system design and non-functional requirements.

• Leadership: Develop into a Tech Lead or Engineering Manager by mentoring junior engineers, leading project initiatives, and managing team processes.

• Cross-Functional Mobility: Opportunities to move into specialized roles within data engineering, cloud engineering, or security engineering, leveraging foundational experience.

• Continuous Learning: Access to extensive internal training, external certifications, and industry conferences to stay abreast of evolving technologies, including AI in software development.

📝 Enhancement Note: This role offers a clear path for technical growth within a large financial institution. The emphasis on full-stack development and data integration provides a solid foundation for various specialized engineering careers. The company's investment in training and development, coupled with the opportunity to work on complex, high-impact projects, makes it an attractive environment for ambitious software engineers.

🌐 Work Environment

Office Type: On-site at JPMorgan Chase's Mumbai office. This implies a traditional office setting designed for collaboration and productivity.

Office Location(s): Ventura Towers, Hiranandani Business Park, Powai, Mumbai. This is a well-established business district, likely offering good connectivity and amenities.

Workspace Context:

• The workspace is expected to be collaborative, with opportunities for direct interaction with team members, fostering a strong sense of team synergy.

• Access to modern technology and tools, including enterprise-authorized AI coding assist tools, will be provided to enhance productivity and innovation.

• The environment will be conducive to focused work on complex software development tasks, with dedicated spaces for team meetings and technical discussions.

Work Schedule: Standard 40-hour work week, typically within business hours (Asia/Kolkata time). However, given the nature of financial services and potential for global operations, occasional flexibility may be required to address critical issues or meet project deadlines.

📝 Enhancement Note: The on-site requirement suggests a preference for direct team collaboration and adherence to the structured environment typical of large financial institutions. Candidates should be prepared for a professional office setting where interaction, security protocols, and focused work are prioritized.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter call to assess basic qualifications, experience, and cultural fit.

• Technical Assessment: May involve coding challenges (live coding or take-home), focusing on

Java, data structures, algorithms, and problem-solving skills relevant to full-stack development.

• Technical Interviews: Multiple rounds with engineering team members and leads, delving into:

  • Core Java and Spring Boot concepts.
  • Frontend framework knowledge (React, Angular, Vue).
  • Database design and SQL query optimization.
  • Microservices architecture and API design.
  • CI/CD practices and Git usage.
  • Experience with data technologies (Kafka, Spark if applicable).
  • Understanding and application of AI coding assist tools.
  • Problem-solving scenarios and system design discussions.

• Behavioral Interview: Assessing cultural fit, teamwork, communication skills, and alignment with JPMorgan Chase values.

• Final Round: Potentially with a senior manager or director for final approval.

Portfolio Review Tips:

• Showcase Full-Stack Proficiency: Include projects demonstrating seamless integration of backend Java services with frontend UIs.

• Highlight Data Integration: Present projects involving Kafka, Spark, or other data streaming/processing technologies, explaining the architecture and impact.

• Demonstrate AI Tool Usage: If possible, include examples or discuss how you've utilized AI coding assist tools, focusing on efficiency gains and validation processes.

• Articulate Problem-Solving: For each project, clearly explain the problem, your technical approach, the challenges faced, and the solutions implemented.

• Quantify Impact: Use metrics where possible to demonstrate the success of your contributions (e.g., performance improvements, reduction in bugs, increased development speed).

• Focus on SDLC Practices: Highlight your experience with CI/CD, automated testing, Git, and code reviews.

Challenge Preparation:

• Coding Challenges: Practice coding problems on platforms like LeetCode, HackerRank, focusing on Java, algorithms, and data structures. Be prepared for live coding sessions.

• System Design: Review common system design patterns for microservices, APIs, and data pipelines. Consider scalability, availability, and fault tolerance.

• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) responses for common behavioral questions related to teamwork, problem-solving, and handling challenges.

• AI Tool Preparedness: Be ready to discuss your experience with AI coding tools, including how you use them, how you validate their output, and your understanding of responsible AI practices.

📝 Enhancement Note: Candidates should prepare to demonstrate a strong command of full-stack development, with a particular emphasis on data integration technologies like Kafka and Spark, and the ability to leverage AI coding tools responsibly. The interview process will likely be rigorous, assessing both technical depth and problem-solving capabilities.

🛠 Tools & Technology Stack

Primary Tools:

• <strong>Java</strong>: Core programming language.

• <strong>Spring / Spring Boot</strong>: Framework for building enterprise Java applications and microservices.

• Modern UI Frameworks: <strong>React, Angular, or Vue</strong> for frontend development.

• <strong>SQL</strong> Databases: PostgreSQL, Oracle for data persistence and management.

• Version Control: <strong>Git</strong> for source code management.

• CI/CD Tools: Jenkins, GitLab CI, or similar for automated builds and deployments. Analytics & Reporting:

• Observability Tools: OpenTelemetry concepts for logging, metrics, and tracing.

• Data Platforms: Integration with data lakes (Snowflake, Databricks, Hive) and data warehousing solutions. CRM & Automation:

• Messaging/Streaming: <strong>Kafka</strong> for real-time data streaming.

• Data Processing: <strong>Spark</strong> for large-scale data processing.

• Orchestration: <strong>Airflow</strong> for workflow management.

• Containerization: <strong>Docker</strong> for packaging applications.

• Orchestration: Kubernetes for managing containerized applications.

📝 Enhancement Note: The technology stack is comprehensive, covering core Java development, modern frontend technologies, robust data integration tools (Kafka, Spark), and essential DevOps practices (CI/CD, Docker, Kubernetes). Proficiency in these areas, especially Kafka and Spark, is a significant plus and indicates the complexity and scale of the systems being developed. The explicit mention of AI-assisted development tools suggests their integration into the daily workflow.

👥 Team Culture & Values

Operations Values:

• <strong>Excellence & Innovation</strong>: Striving for high-quality, secure, and efficient software solutions, while embracing new technologies like AI-assisted development.

• <strong>Integrity & Trust</strong>: Upholding the highest ethical standards in a regulated financial environment, ensuring data security and reliability.

• <strong>Collaboration & Teamwork</strong>: Working effectively across teams to achieve common goals, sharing knowledge and supporting colleagues.

• <strong>Customer Focus</strong>: Building and maintaining systems that deliver value to internal and external clients, ensuring a positive user experience.

• <strong>Accountability & Ownership</strong>: Taking responsibility for one's work, from development through production support, and driving solutions to completion. Collaboration Style:

• Agile and iterative, with frequent communication and feedback loops within the immediate team.

• Cross-functional collaboration with data teams, security, architecture, and product management to ensure comprehensive solutions.

• Emphasis on code reviews and knowledge sharing to maintain code quality and foster continuous learning.

• A structured approach to problem-solving, often involving detailed analysis and documentation of findings and solutions.

📝 Enhancement Note: JPMorgan Chase, as a leading financial institution, instills values of integrity, excellence, and security. For operations professionals, this means a commitment to robust processes, meticulous attention to detail, and a strong sense of responsibility. The collaborative style, combined with the structured environment, fosters professional growth while ensuring operational rigor.

⚡ Challenges & Growth Opportunities

Challenges:

• <strong>Complexity of Financial Systems</strong>: Navigating the intricate requirements and regulations of the financial services industry.

• <strong>Scalability and Performance Demands</strong>: Ensuring systems can handle high transaction volumes and demanding performance SLAs.

• <strong>Integrating Legacy and Modern Systems</strong>: Balancing the need for new technology adoption with the maintenance of existing infrastructure.

• <strong>Security and Compliance Adherence</strong>: Meeting stringent security protocols and regulatory requirements in every development phase.

• <strong>Effective Use of AI Tools</strong>: Learning to optimally leverage AI coding assistants while maintaining critical oversight and validation. Learning & Development Opportunities:

• <strong>Advanced Technical Training</strong>: Deep dives into Java, Spring, Kafka, Spark, cloud technologies, and AI/ML applications in software engineering.

• <strong>Certifications</strong>: Opportunities to pursue industry-recognized certifications in relevant technologies.

• <strong>Mentorship Programs</strong>: Access to experienced engineers and leaders for guidance and career development.

• <strong>Internal Mobility</strong>: Pathways to explore roles in specialized engineering domains or management.

• <strong>Exposure to Cutting-Edge Technology</strong>: Working with and learning about emerging technologies and their application in finance.

📝 Enhancement Note: This role presents significant opportunities for growth by tackling complex challenges within a highly regulated industry. The explicit inclusion of AI tools suggests a forward-looking approach to development, offering unique learning experiences. Candidates who are eager to learn, adapt, and contribute to high-impact projects will find this role particularly rewarding.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex full-stack application you've built. What were the key architectural decisions, and how did you ensure scalability and security?" (Focus on Java, UI, data integration, and non-functional requirements).

• "How have you used AI-assisted development tools in your workflow? Provide a specific example of how it improved your productivity or code quality, and how you validated the output." (Demonstrate practical experience and critical evaluation).

• "Walk me through a challenging production issue you resolved involving UI, backend services, and data. What was your troubleshooting process?" (Highlight problem-solving methodology and cross-functional understanding). Company & Culture Questions:

• "What do you know about JPMorgan Chase's role in the Commercial & Investment Bank, and how do you see technology contributing to its success?" (Show research and strategic thinking).

• "How do you approach collaboration within an agile team, especially when dealing with diverse technical backgrounds or cross-functional dependencies?" (Emphasize teamwork and communication).

• "How do you ensure the security and integrity of data in your software development practices, especially in a financial context?" (Highlight awareness of industry-specific concerns). Portfolio Presentation Strategy:

• Structured Case Studies: For each project, clearly outline the business problem, your role, the technical stack used (Java, UI framework, databases, Kafka/Spark if applicable), your specific contributions, challenges overcome, and quantifiable results.

• Demonstrate Full-Stack Flow: Visually or verbally illustrate how your frontend and backend components interact, and how data flows through the system.

• Highlight AI Tool Integration: If possible, show examples or discuss scenarios where AI tools were used, explaining the benefits and your validation process.

• Focus on Process: Explain your SDLC practices, including testing strategies, CI/CD implementation, and code review contributions.

• Be Prepared for Deep Dives: Anticipate detailed technical questions about your project choices and implementation details.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong grasp of full-stack development, practical experience with Java and modern UI frameworks, and a clear understanding of data integration technologies like Kafka and Spark. Crucially, candidates must be prepared to discuss their experience with AI coding tools, emphasizing responsible usage and validation.

📌 Application Steps

To apply for this software engineering position:

• Submit your application through the provided Oracle Cloud portal link.

• Customize Your Resume: Tailor your resume to highlight your 2+ years of experience in Java, full-stack development, and any experience with Spark, Kafka, or modern UI frameworks. Quantify achievements where possible.

• Prepare Your Portfolio: Curate your best projects, focusing on those that showcase end-to-end solutions, data integration, and any application of AI coding tools. Be ready to articulate your process and impact.

• Practice Technical Concepts: Review core Java, Spring Boot, frontend fundamentals, SQL, and common algorithms. Brush up on CI/CD and Git principles.

• Research JPMorgan Chase: Understand the company's mission, values, and its role within the Commercial & Investment Bank. Familiarize yourself with their approach to technology and innovation.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.