Software Engineer III - Java, UI, SQL, Kafka, Spark

📍 Job Overview

Job Title: Software Engineer III - Java, UI, SQL, Kafka, Spark

Company: JPMorgan Chase & Co.

Location: Mumbai, Maharashtra, India

Job Type: Full time

Category: Software Engineering / Full-Stack Development

Date Posted: 2026-09-22

Experience Level: Mid-Level (2-5 years)

Remote Status: On-site

🚀 Role Summary

• Design, build, and maintain robust end-to-end full-stack software solutions leveraging Java technologies and modern UI frameworks.

• Develop and support scalable backend services and APIs, ensuring secure and efficient data integration and error handling.

• Utilize enterprise-authorized AI coding assist tools to enhance code quality, accelerate delivery, and improve productivity throughout the software development lifecycle.

• Collaborate with cross-functional teams to enable data-centric use cases by integrating with data platforms, services, and event streams, ensuring data quality and observability.

• Apply rigorous engineering practices including code reviews, automated testing, CI/CD, performance tuning, and production support to maintain system integrity and reliability.

📝 Enhancement Note: This role is categorized under Software Engineering with a strong emphasis on Full-Stack Development, given the explicit mention of both Java backend technologies and modern UI frameworks. The inclusion of AI coding assist tools highlights a forward-thinking approach to development within JPMorgan Chase. The role's focus on data platforms and event streams, alongside core backend and frontend responsibilities, indicates a need for engineers who can bridge application development with data infrastructure.

📈 Primary Responsibilities

• Architect, develop, and deploy end-to-end full-stack solutions using Java, modern UI frameworks, and associated libraries.

• Build and maintain secure, high-performance backend services and APIs, implementing robust authentication, authorization, and integration patterns.

• Integrate AI coding assist tools into the development workflow for code generation, unit test creation, refactoring, and documentation, while rigorously validating outputs.

• Develop responsive, accessible, and reusable frontend components, adhering to established engineering and design standards.

• Facilitate data-driven functionalities by integrating with data platforms, data services, and real-time event streams (e.g., Kafka) to expose reliable datasets.

• Partner with the data horizontal team to enhance data quality, implement observability, track lineage, and ensure data governance compliance for applications.

• Champion and implement strong software engineering practices, including comprehensive code reviews, extensive unit and integration testing, efficient CI/CD pipelines, performance optimization, and proactive production support.

• Actively participate in system design discussions, contributing to the definition and implementation of non-functional requirements such as security, resiliency, scalability, and low latency.

• Diagnose and resolve complex production issues spanning UI, backend services, and data interactions, developing and deploying sustainable fixes and automation solutions.

📝 Enhancement Note: The responsibilities emphasize a full-stack development lifecycle, from design and build to support and troubleshooting. The integration of AI tools is a key responsibility, requiring engineers to not only use them but also validate their outputs and understand responsible AI practices. The collaboration with data teams and focus on data platforms (Kafka, Spark) suggest a need for engineers who can handle data-intensive applications.

🎓 Skills & Qualifications

Education: Formal training or certification in software engineering concepts is required, demonstrating a foundational understanding.

Experience: A minimum of 2+ years of applied professional software engineering experience with a strong track record in full-stack delivery.

Required Skills:

• Proficient in Java and enterprise backend development, with specific experience in frameworks like Spring / Spring Boot.

• Proven experience in building microservices and designing well-defined APIs, particularly RESTful interfaces.

• Solid understanding of modern frontend development principles and hands-on experience with at least one major framework (e.g., React, Angular, or Vue), including proficiency in HTML, CSS, TypeScript, and JavaScript.

• Strong knowledge of relational databases (e.g., PostgreSQL, Oracle), including effective schema design and query optimization techniques using SQL.

• Practical experience with core software engineering practices, including setting up and managing CI/CD pipelines, implementing automated testing strategies, and utilizing version control systems like Git.

• Hands-on experience with enterprise-authorized AI-assisted software development tools (e.g., for coding, testing, troubleshooting, or documentation) and the ability to critically evaluate and validate AI-generated outputs.

• Understanding of responsible AI use in engineering workflows, including data sensitivity, secure input/output handling, and adherence to resiliency and security standards.

• Practical knowledge of critical security concepts (e.g., OAuth2, JWT, secure coding practices, secrets management) and requirements for production readiness.

• Excellent problem-solving abilities, a collaborative spirit for cross-team work, and strong written and verbal communication skills. Preferred Skills:

• Experience with data engineering and data platform integrations, specifically with technologies like:

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

• Familiarity with data governance principles, including metadata management, lineage tracking, data quality checks, access controls, and auditability.

• Experience with observability tooling such as centralized logging, metrics aggregation, and distributed tracing (e.g., understanding OpenTelemetry concepts).

• Proficiency in containerization technologies like Docker and orchestration platforms such as Kubernetes (or equivalents).

• Exposure to financial services domains, particularly loan origination/servicing systems or regulated financial workflows.

📝 Enhancement Note: The distinction between required and preferred skills is crucial. While core Java, UI, SQL, and CI/CD are mandatory, the preferred skills highlight a significant advantage for candidates with data engineering and distributed systems experience (Kafka, Spark, Airflow, Docker, Kubernetes). The emphasis on AI tools and responsible AI practices is a modern requirement that candidates must address.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of end-to-end full-stack application development, showcasing integrated backend services and frontend user interfaces.

• Case studies detailing the design and implementation of microservices and APIs, with explanations of architectural decisions and problem-solving approaches.

• Projects that highlight experience with database design and SQL query optimization, including examples of schema evolution and performance tuning.

• Evidence of contributions to CI/CD pipelines, automated testing frameworks, and version control workflows (Git), illustrating efficiency and reliability in development processes.

• Examples of integrating with or managing data streams and data platforms, showcasing understanding of data flow and processing. Process Documentation:

• Workflow designs and optimization strategies for common development processes, such as feature development, bug fixing, and deployment.

• Implementation details of automation scripts and tools used to streamline development, testing, or operational tasks.

• Metrics and analysis reports demonstrating the impact of implemented processes on key performance indicators like development velocity, code quality, and system stability.

📝 Enhancement Note: For a mid-level software engineering role, a portfolio should not just list technologies but demonstrate practical application. The emphasis here is on showcasing the ability to build complete solutions, optimize processes, and integrate with data infrastructure, aligning with the job's full-stack and data-centric aspects.

💵 Compensation & Benefits

Salary Range: For a Software Engineer III role with 2-5 years of experience in Mumbai, India, the estimated annual salary range is ₹12,00,000 to ₹25,00,000 (Indian Rupees). This range is based on industry benchmarks for mid-level software engineers in major Indian tech hubs, considering the specific technology stack (Java, UI frameworks, Kafka, Spark) and the employer's standing as a global financial institution. Factors such as specific skill proficiency, interview performance, and the exact scope of responsibilities can influence the final offer.

Benefits:

• Comprehensive health insurance coverage for employees and dependents.

• Retirement savings plans and employee stock purchase options.

• Generous paid time off (PTO), including vacation days, sick leave, and public holidays.

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

• Access to employee assistance programs for mental health and well-being support.

• Relocation assistance may be available for candidates moving to Mumbai.

• Performance-based bonuses and incentives.

Working Hours: The standard working hours are approximately 40 hours per week, typical for a full-time role in the IT sector. While the role is on-site, there may be flexibility in start and end times, subject to team coordination and operational needs. Occasional work outside standard hours may be required to address critical production issues or meet project deadlines.

📝 Enhancement Note: Salary ranges for Mumbai are highly competitive, especially for roles requiring specialized skills like Kafka and Spark. JPMorgan Chase, as a large multinational, typically offers robust benefits packages that extend beyond standard offerings. The on-site nature requires specific consideration for commute and work-life balance.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (Commercial & Investment Banking). JPMorgan Chase operates within a highly regulated and dynamic global financial market, necessitating stringent security, compliance, and reliability standards in all its technology operations.

Company Size: Large (Over 10,000 employees). As a global financial institution, JPMorgan Chase has a vast organizational structure, offering numerous opportunities for specialization and career progression across diverse business lines and technology functions.

Founded: 2000 (following the merger of Chase Manhattan Corporation and J.P. Morgan & Co.). The company has a long history of financial innovation and is a leading player in global markets, emphasizing stability, trust, and technological advancement.

Team Structure:

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

• Teams are typically cross-functional, comprising software engineers, quality assurance professionals, product owners, and business analysts.

• Reporting structures are likely hierarchical but operate within an agile framework that promotes collaboration and shared ownership.

• Close collaboration with other technology teams, including data horizontals, infrastructure, and security, is expected. Methodology:

• Agile Development: Teams operate using Agile methodologies (e.g., Scrum, Kanban) to deliver software iteratively and respond to changing business needs.

• Data-Driven Decision Making: Emphasis on leveraging data for insights, performance monitoring, and continuous improvement of applications and processes.

• DevOps Culture: Integration of development and operations practices to ensure seamless delivery, deployment, and maintenance of software.

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

📝 Enhancement Note: JPMorgan Chase's culture in its technology divisions often balances rigorous, compliance-driven financial services standards with modern, agile software development practices. The large company size means career paths can be diverse, but navigating them requires proactive engagement. The emphasis on AI tools suggests a company actively investing in future technologies.

📈 Career & Growth Analysis

Operations Career Level: Software Engineer III (Mid-Level). This level signifies a professional who can independently handle complex development tasks, contribute significantly to system design, mentor junior engineers, and take ownership of critical components. The role requires a strong foundation in core engineering principles and practical experience in full-stack development, data integration, and modern tooling.

Reporting Structure: Likely reports to a Software Engineering Manager or a Lead Engineer, with direct collaboration and reporting lines within an agile team. May also have indirect reporting to product managers or business stakeholders for project-specific alignment.

Operations Impact: This role directly impacts the firm's technological capabilities within the Commercial & Investment Bank. By building and maintaining secure, scalable, and efficient software solutions, the engineer contributes to the operational efficiency, client service quality, and revenue-generating potential of critical banking systems. The integration of data platforms and AI tools further enhances the analytical and predictive power of these systems.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific areas like distributed systems (Kafka, Spark), cloud-native development, advanced UI frameworks, or data engineering.

• Leadership Development: Progress to Senior Software Engineer, Tech Lead, or Architect roles, taking on more design responsibility and team mentorship.

• Cross-Functional Mobility: Opportunities to move into related roles such as DevOps Engineering, Data Science, or Product Management within the broader technology organization.

• Management Track: Potential to move into engineering management roles, leading teams and strategic technology initiatives.

• Continuous Learning: Access to extensive internal training, external certifications, and opportunities to engage with cutting-edge technologies like AI and machine learning.

📝 Enhancement Note: The "III" in Software Engineer III typically denotes a solid mid-level professional capable of independent work and some mentorship. JPMorgan Chase, as a large institution, offers structured career paths, but growth often depends on demonstrated initiative and skill development in areas aligned with the company's strategic technology investments, such as AI and advanced data platforms.

🌐 Work Environment

Office Type: The role is on-site, implying a traditional office environment within JPMorgan Chase's Mumbai facilities. This setting typically offers dedicated workspaces, meeting rooms, and collaborative areas.

Office Location(s): Ventura Towers, Hiranandani Business Park, Powai, Mumbai. This is a prominent business district in Mumbai, offering good connectivity and access to amenities.

Workspace Context:

• Collaborative Environment: Expect a dynamic office setting designed to foster teamwork, with open-plan areas, meeting rooms, and breakout spaces for discussions.

• Technology Access: State-of-the-art technology infrastructure, including high-performance workstations, reliable network connectivity, and access to the firm's extensive software and toolset.

• Team Interaction: Regular opportunities for face-to-face interaction with team members, fostering strong working relationships and efficient knowledge sharing.

Work Schedule: The standard work schedule is approximately 40 hours per week. While the role is on-site, there may be a degree of flexibility in daily start and end times, subject to team coordination and business needs. However, the nature of financial services technology can sometimes necessitate extended hours or on-call duties to ensure system stability and address critical issues.

📝 Enhancement Note: An on-site role in a major business park like Hiranandani means candidates should consider commute times and the professional office culture typical of large financial institutions. The environment is geared towards collaboration and productivity, with direct access to colleagues and resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review applications and conduct a brief screening call to assess basic qualifications and cultural fit.

• Technical Assessments: Candidates will likely undergo one or more technical interviews. These may include:

  • Coding Challenges: Live coding exercises focusing on Java, data structures, algorithms, and SQL.
  • System Design Interview: Discussions on designing scalable, resilient systems, often involving microservices, APIs, and data integration.
  • Technical Q&A: In-depth questions about the required skills, including experience with Kafka, Spark, Spring Boot, UI frameworks, and CI/CD.

• Behavioral/Situational Interviews: Questions designed to assess problem-solving skills, teamwork, communication, and alignment with JPMorgan Chase's values. This may include discussing how you've used AI coding tools.

• Final Round: A meeting with senior leadership or the hiring manager to discuss overall fit, career aspirations, and finalize the offer.

Portfolio Review Tips:

• Curate Select Projects: Focus on 2-3 projects that best showcase your full-stack capabilities, Java backend expertise, UI development, and any experience with data platforms (Kafka, Spark).

• Highlight Process & Impact: For each project, clearly articulate the problem statement, your role, the technologies used, the development process (including CI/CD, testing), and the quantifiable impact or outcome.

• Demonstrate AI Tool Usage: If possible, include examples or discuss how you've leveraged AI coding assistants, explaining your approach to validation and responsible use.

• Prepare for Deep Dives: Be ready to discuss architectural decisions, trade-offs, challenges faced, and how you overcame them. For database projects, be prepared to write and optimize SQL queries.

Challenge Preparation:

• Algorithm & Data Structures: Brush up on common algorithms (sorting, searching, graph traversal) and data structures (arrays, linked lists, trees, hash maps). LeetCode (Easy/Medium) is a good resource.

• Java Fundamentals: Review core Java concepts, object-oriented programming principles, and common Java libraries/frameworks (especially Spring Boot).

• SQL Proficiency: Practice writing complex SQL queries, understanding joins, subqueries, indexing, and query optimization.

• System Design Basics: Prepare to discuss concepts like microservices, RESTful APIs, message queues, database choices (SQL vs. NoSQL), caching, and scalability.

• AI Tooling: Be ready to discuss your experience with AI coding tools, their benefits, limitations, and how you ensure code quality and security when using them.

📝 Enhancement Note: The interview process at large financial institutions like JPMorgan Chase is typically rigorous and multi-stage. Candidates should prepare thoroughly for both technical and behavioral aspects. Demonstrating practical experience with AI coding tools and a solid understanding of data platforms will be key differentiators.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Java (primary backend), TypeScript/JavaScript (primary frontend)

• Backend Frameworks: Spring / Spring Boot

• Frontend Frameworks: React, Angular, or Vue.js

• Databases: PostgreSQL, Oracle (SQL proficiency required)

• Version Control: Git

• Build Tools: Maven/Gradle

• AI-Assisted Development: Enterprise-authorized AI coding assist tools (specifics may vary, but candidates should be prepared to discuss their use).

Analytics & Reporting:

• CI/CD Tools: Jenkins, GitLab CI, Azure DevOps, or similar

• Observability Tools: Tools for logging, metrics, and tracing (e.g., ELK stack, Prometheus, Grafana, OpenTelemetry concepts)

CRM & Automation:

• Messaging/Streaming: Kafka (preferred)

• Data Processing: Spark (preferred)

• Containerization: Docker

• Orchestration: Kubernetes (preferred)

• Workflow Orchestration: Airflow (preferred)

📝 Enhancement Note: The technology stack is comprehensive, covering core Java development, modern frontend technologies, relational databases, and essential DevOps tools. The "preferred" technologies (Kafka, Spark, Kubernetes, Airflow) are highly desirable and indicate the direction of the firm's data and distributed systems architecture. Familiarity with AI coding tools is a direct requirement.

👥 Team Culture & Values

Operations Values:

• Integrity & Trust: Upholding the highest ethical standards and maintaining client trust is paramount in financial services. Operations professionals are expected to act with integrity in all dealings.

• Innovation & Agility: While operating in a regulated environment, the firm values continuous improvement, embracing new technologies (like AI coding tools), and adopting agile methodologies to stay competitive.

• Excellence & Accountability: A commitment to delivering high-quality, reliable solutions and taking ownership of one's work and its impact on the business.

• Collaboration & Inclusion: Fostering a diverse and inclusive environment where teamwork, open communication, and mutual respect are encouraged, enabling collective problem-solving.

• Client Focus: Understanding and prioritizing the needs of internal and external clients, ensuring technology solutions directly support business objectives and enhance client experience.

Collaboration Style:

• Cross-functional Integration: Engineers are expected to work closely with product managers, QA, data teams, and other engineering groups to deliver cohesive solutions.

• Code Review Culture: A strong emphasis on peer code reviews to ensure quality, share knowledge, and maintain coding standards.

• Feedback Exchange: Openness to providing and receiving constructive feedback as part of the agile development process and continuous improvement initiatives.

• Knowledge Sharing: Encouraging the sharing of best practices, technical insights, and lessons learned through internal forums, documentation, and pair programming.

📝 Enhancement Note: JPMorgan Chase's culture emphasizes a blend of traditional financial industry rigor with modern technology practices. The values highlight professionalism, innovation, and teamwork, crucial for success in a complex and regulated environment.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Complex Systems: Understanding and integrating with legacy systems alongside modern microservices and data platforms can be challenging.

• Balancing Innovation with Regulation: Implementing new technologies and agile practices while adhering to strict financial industry regulations and security standards.

• Production Stability: Ensuring the high availability, performance, and security of critical financial systems, especially during high-volume periods.

• Data Integration Complexity: Managing diverse data sources, ensuring data quality, and implementing real-time data pipelines for complex financial workflows.

• AI Tool Adoption: Effectively integrating and validating AI-generated code, ensuring it meets security, performance, and compliance requirements.

Learning & Development Opportunities:

• Advanced Technical Training: Access to specialized courses and workshops on Java, cloud computing, data engineering technologies (Kafka, Spark), and AI/ML.

• Industry Certifications: Support for obtaining relevant certifications in cloud platforms, data technologies, or cybersecurity.

• Mentorship Programs: Opportunities to be mentored by senior engineers and architects, as well as to mentor junior team members.

• Internal Knowledge Sharing Sessions: Regular tech talks, brown bag sessions, and internal forums to learn about new technologies and best practices.

• Access to Cutting-Edge Tools: Hands-on experience with enterprise-grade AI coding assistants and other advanced developer tools.

📝 Enhancement Note: The challenges reflect the reality of working in a large, regulated financial institution with a diverse technology landscape. The growth opportunities are substantial, driven by the firm's investment in technology and employee development.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex full-stack application you designed and built. What were the key challenges, and how did you overcome them using Java, your chosen UI framework, and any data integration patterns?" (Focus on architecture, problem-solving, and technology choices.)

• "How have you leveraged AI-assisted coding tools in your development process? What are the benefits and limitations you've observed, and how do you ensure the quality and security of AI-generated code?" (Prepare to discuss specific tools, your workflow, and validation methods.)

• "Walk me through your experience with CI/CD pipelines. How do you ensure automated testing is effective, and what steps do you take to maintain code quality and facilitate smooth deployments?" (Highlight your understanding of DevOps practices and automation.)

• "Describe a situation where you had to integrate with a data platform or stream (like Kafka). What were the requirements, and how did you ensure data reliability and efficient processing?" (Focus on data engineering aspects and practical implementation.) Company & Culture Questions:

• "What do you know about JPMorgan Chase's technology initiatives, particularly in areas like AI or data analytics in financial services?" (Research recent company news and technology blogs.)

• "How do you approach collaboration within an agile team, especially when working with different disciplines like product management or data engineers?" (Emphasize teamwork, communication, and understanding different perspectives.)

• "Describe a time you faced a significant technical challenge in a production environment. How did you troubleshoot, what was the resolution, and what did you learn?" (Showcase problem-solving skills and ability to learn from experience.)

• "How do you stay updated with the latest advancements in software engineering, including new frameworks, tools, and AI technologies?" (Demonstrate a commitment to continuous learning.) Portfolio Presentation Strategy:

• Structure: Organize your portfolio by project. For each project, clearly state the objective, your role, the technologies used, the process (design, development, testing, deployment), and the outcome or impact.

• Quantify Impact: Whenever possible, use metrics to demonstrate the value of your work (e.g., performance improvements, reduction in errors, increased user engagement).

• Showcase Full-Stack: Ensure your examples clearly illustrate your ability to work across both backend (Java, APIs, databases) and frontend (UI components, user experience).

• Highlight AI Integration: If you have specific examples of using AI tools, present them clearly, detailing your approach to using and validating the AI-generated outputs.

• Prepare for Technical Questions: Be ready to answer detailed technical questions about your projects, architectural decisions, and the trade-offs you made.

📝 Enhancement Note: Interview preparation should focus on demonstrating practical application of skills, problem-solving abilities, and an understanding of the financial services context. Highlighting experience with AI tools and data platforms will be critical for this role.

📌 Application Steps

To apply for this software engineering position:

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

• Tailor Your Resume: Highlight your experience with Java, Spring Boot, modern UI frameworks (React, Angular, Vue), SQL, CI/CD, and any exposure to Kafka or Spark. Explicitly mention your experience with AI-assisted development tools and responsible AI practices.

• Prepare Your Portfolio: Curate 2-3 strong projects that showcase your full-stack development capabilities, database skills, and experience with data integration. Be ready to discuss your contributions, technical decisions, and the impact of your work.

• Practice Technical Questions: Rehearse coding challenges, system design concepts, and SQL query writing. Be prepared to discuss your approach to using AI coding tools and validating their outputs.

• Research JPMorgan Chase: Understand the company's mission, values, and recent technology initiatives, particularly in financial services and AI. Be ready to articulate why you are a good fit for their culture and this specific role.

⚠️ 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.