Lead Software Engineer - Data Engineer

JPMorgan Chase & Co.; Hyderabad; Presencial; INR 3.000.000 - 6.000.000; Jornada completa

Descripción de la vacante

JPMorgan Chase & Co. in India is seeking a Lead Software Engineer to join the Consumer and Community Banking technology team. You will be part of an agile group that builds trusted, scalable data products and ensures secure operations across the data lifecycle.

You will design end-to-end data architectures using Spark, AWS Glue, Kafka, and Iceberg; implement IaC; and drive AI-assisted engineering practices to improve quality, delivery speed, and reliability across enterprise-scale data pipelines.

Formación

• Formal training or certification in software engineering concepts.
• 5+ years of applied experience in data engineering or similar.
• Experience with AI-assisted development tools and secure coding practices.

Responsabilidades

• Build automated, self-healing data operations with AI-driven workflows.
• Design and deploy data solutions using AWS S3, Lambda, EKS, and Step Functions.
• Implement IaC for reproducible, scalable deployments.
• Promote enterprise AI-assisted engineering practices and code quality.
• Develop data ingestion, transformation, and analytics with Spark, Glue, Iceberg.
• Build batch and real-time pipelines with Kafka and Flink, enabling Data Lake governance.
• Define data architecture standards for batch and real-time pipelines.

Conocimientos

Python

Scala/Java

SQL

AI-assisted development

DataOps

Security awareness

Herramientas

AWS

S3

Lambda

EKS

Step Functions

AWS Glue

Apache Spark

Apache Iceberg

Kafka

Flink

Descripción del empleo

Job Description:

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Lead Software Engineer at JPMorganChase within the Consumer and community banking technology team, 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.

Job responsibilities

• Build automated, self-healing data operations using agentic frameworks and AI-driven workflows for intelligent monitoring, optimization, and remediation
• Design and deploy data solutions using AWS services including S3, Lambda, EKS (Elastic Kubernetes Service), and Step Functions
• Implement Infrastructure as Code (IaC) practices for reproducible and scalable deployments
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Build and optimize scalable data ingestion, transformation, and analytics solutions using Apache Spark, AWS Glue, and Apache Iceberg
• Develop and manage both batch and real-time data pipelines leveraging Apache Kafka and Apache Flink for streaming data processing. Implement Data Lake architectures with efficient data organization, partitioning, and governance strategies
• Design and architect end-to-end data solutions from ingestion to analytics, ensuring scalability, reliability, and performance at enterprise scale
• Define data architecture standards and best practices for batch and real-time data processing pipelines

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 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
• Experience with modern data engineering practices including DataOps, data observability, and data governance
• Deep expertise in data ingestion, transformation, and analytics using Apache Spark, AWS Glue, Apache Iceberg, and Data Lake architectures
• Proven experience designing and managing batch and real-time data pipelines with Apache Kafka and Apache Flink
• Strong hands-on experience with AWS cloud services including S3, Lambda, EKS, and Step Functions
• Experience building automated solutions using agentic frameworks and AI-driven workflows for intelligent data operations
• Advanced proficiency in programming languages such as Python, Scala, or Java with strong SQL skills
• Demonstrated ability to architect end-to-end data solutions from ingestion to analytics at scale