Lead Software Engineer - Lead Data Architect

JPMorganChase · Mumbai · 5+ yrs experience · Posted 2026-07-18

Tech stack: AWS, Cassandra, GraphQL, Java, Kafka, MongoDB, PostgreSQL

Apply on the company site · Get a referral for this role

JPMorganChase salary & ratings · JPMorganChase interview process · More live openings

About the role

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Responsibilities:
- Define and drive end-to-end architecture for complex, distributed, high-throughput systems across the portfolio; shape technology strategy and inform budget and investment prioritization with senior leadership.
- Lead design reviews, architecture governance, and technical decisions; establish enterprise-wide patterns, standards, and reference architecture, and represent Engineering & Architecture in senior governance forums (architecture review boards, risk/security committees, regulatory engagements).
- Architect cloud-native solutions on AWS with operational rigor across reliability, scalability, security, and cost.
- Design and optimize relational and NoSQL data solutions for performance, scale, and reliability—including schema design, indexing, replication, sharding/partitioning, and query optimization.
- Build and integrate AI/ML and GenAI into production platforms—LLMs, RAG, embeddings, vector databases, and MLOps for lifecycle management, monitoring, and governance.
- Drive adoption of enterprise-authorized AI-assisted engineering practices (code review/refactoring, test acceleration, incident/root-cause analysis) with consistent validation standards (secure coding, peer review, automated testing) and reuse of effective patterns.
- Develop senior technical talent and partner across functions—lead hiring, calibration, and succession for principal/staff engineers, grow the architecture community of practice, and align with Product, Risk, Security, and Infrastructure leaders on outcomes, regulatory expectations, and risk posture.
- Own non-functional
Qualifications:
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Bachelor’s degree or equivalent in computer science/information technology or software engineering concepts fields and 10+ years applied experience spanning data architecture and software development.
- Deep expertise in data architecture and database technologies across relational (Oracle, PostgreSQL) and NoSQL (Cassandra, MongoDB), including data modeling, schema design, indexing strategy, replication, sharding/partitioning, and query optimization at scale.
- Proven experience designing large-scale data platforms — data pipelines, streaming/ingestion, warehousing/lakehouse patterns, and data governance, lineage, and quality.
- Working knowledge in Java (ideally Java 17+), or any other language and experience with microservices and event-driven architectures, including API design (REST, gRPC, GraphQL) and messaging/streaming such as Kafka.
- Demonstrable experience with AWS — designing, deploying, and operating production data and application workloads at scale, with clear ownership of reliability and cost outcomes.
- Hands-on experience integrating AI/ML and GenAI into enterprise applications, including LLMs, RAG pipelines, embeddings, vector stores/databases, model evaluation, and production monitoring (MLOps).
- Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong design and architectural thinking — making trade-offs explicit and balancing speed, risk, maintainability, and total cost of ownership across data and distributed systems.
- Good grasp of DevOps and platform/data engineering practices: CI/CD, Infrastructure as Code (IaC) and observability tooling (logs, metrics, traces).
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Exposure in Financial Services or other highly regulated industries, with practical understanding of auditability, change control, and security expectations.
- Contributions to open-source projects, patents, or published technical work that demonstrates technical depth and community credibility.
- Experience with multi-region, active-active architectures, disaster recovery design, and zero-downtime migrations.
- Familiarity with security frameworks, threat modeling, secure SDLC, and zero-trust architecture.
- Experience leading platform modernization, large-scale cloud migrations, or modernization of legacy monoliths into service-based architectures.

Qualifications

- Formal training or certification on software engineering concepts and 5+ years applied experience
- Bachelor’s degree or equivalent in computer science/information technology or software engineering concepts fields and 10+ years applied experience spanning data architecture and software development.
- Deep expertise in data architecture and database technologies across relational (Oracle, PostgreSQL) and NoSQL (Cassandra, MongoDB), including data modeling, schema design, indexing strategy, replication, sharding/partitioning, and query optimization at scale.
- Proven experience designing large-scale data platforms — data pipelines, streaming/ingestion, warehousing/lakehouse patterns, and data governance, lineage, and quality.
- Working knowledge in Java (ideally Java 17+), or any other language and experience with microservices and event-driven architectures, including API design (REST, gRPC, GraphQL) and messaging/streaming such as Kafka.
- Demonstrable experience with AWS — designing, deploying, and operating production data and application workloads at scale, with clear ownership of reliability and cost outcomes.
- Hands-on experience integrating AI/ML and GenAI into enterprise applications, including LLMs, RAG pipelines, embeddings, vector stores/databases, model evaluation, and production monitoring (MLOps).
- Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong design and architectural thinking — making trade-offs explicit and balancing speed, risk, maintainability, and total cost of ownership across data and distributed systems.
- Good grasp of DevOps and platform/data engineering practices: CI/CD, Infrastructure as Code (IaC) and observability tooling (logs, metrics, traces).
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Exposure in Financial Services or other highly regulated industries, with practical understanding of auditability, change control, and security expectations.
- Contributions to open-source projects, patents, or published technical work that demonstrates technical depth and community credibility.
- Experience with multi-region, active-active architectures, disaster recovery design, and zero-downtime migrations.
- Familiarity with security frameworks, threat modeling, secure SDLC, and zero-trust architecture.
- Experience leading platform modernization, large-scale cloud migrations, or modernization of legacy monoliths into service-based architectures.

Responsibilities

- Define and drive end-to-end architecture for complex, distributed, high-throughput systems across the portfolio; shape technology strategy and inform budget and investment prioritization with senior leadership.
- Lead design reviews, architecture governance, and technical decisions; establish enterprise-wide patterns, standards, and reference architecture, and represent Engineering & Architecture in senior governance forums (architecture review boards, risk/security committees, regulatory engagements).
- Architect cloud-native solutions on AWS with operational rigor across reliability, scalability, security, and cost.
- Design and optimize relational and NoSQL data solutions for performance, scale, and reliability—including schema design, indexing, replication, sharding/partitioning, and query optimization.
- Build and integrate AI/ML and GenAI into production platforms—LLMs, RAG, embeddings, vector databases, and MLOps for lifecycle management, monitoring, and governance.
- Drive adoption of enterprise-authorized AI-assisted engineering practices (code review/refactoring, test acceleration, incident/root-cause analysis) with consistent validation standards (secure coding, peer review, automated testing) and reuse of effective patterns.
- Develop senior technical talent and partner across functions—lead hiring, calibration, and succession for principal/staff engineers, grow the architecture community of practice, and align with Product, Risk, Security, and Infrastructure leaders on outcomes, regulatory expectations, and risk posture.
- Own non-functional

More openings at JPMorganChase