Data Engineer III

JPMorganChase · Bengaluru · Posted 2026-07-04

Tech stack: AWS, Docker, Kafka, Kubernetes, Python, React, SQL, TypeScript

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About the role

We are building the next generation of digital banking and data platforms, and you will play a key role in shaping how data powers critical business decisions. As a Data Engineer, you will work within an agile, collaborative team to design and deliver scalable, secure, and high-quality data solutions. You will help drive innovation by building resilient data systems that support analytics, insights, and customer-focused products.
Responsibilities:
- Build and maintain scalable data pipelines and architectures on cloud platforms, primarily AWS
- Develop, test, and deploy high-quality data and backend solutions with a focus on reliability and performance
- Design and implement microservices and APIs to support data workflows and integrations
- Optimize data processing systems to handle large-scale datasets efficiently and ensure high availability
- Implement data solutions using distributed processing frameworks such as Spark or PySpark
- Apply Agile and DevOps practices, including CI/CD, automation, and infrastructure as code
- Collaborate with stakeholders to understand data requirements and improve data accessibility and usability
- Ensure data quality, integrity, and consistency across systems and platforms
- Support integration of data into analytics and reporting platforms for business insights
- Contribute to building resilient, secure, and scalable systems aligned with engineering best practices
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related discipline
- Hands-on experience in data engineering or backend software engineering
- Strong programming skills in Python with experience in data processing frameworks such as Spark or PySpark
- Experience building and maintaining data pipelines and large-scale data systems (data lake or warehouse environments)
- Knowledge of SQL and experience working with structured and unstructured datasets
- Experience with distributed systems and scalable data architectures
- Familiarity with CI/CD practices, Agile methodologies, and application resiliency
- Experience with data tools such as dbt, Glue, or Trino
- Exposure to orchestration tools such as Airflow or Step Functions and experience with infrastructure as code
- Experience with containerization technologies such as Docker and Kubernetes
- Understanding of cloud platforms, preferably AWS
- Knowledge of streaming and real-time data processing (Kafka, Spark Streaming)
- Experience building automated testing frameworks across unit, integration, and end-to-end levels
- Exposure to modern frontend technologies such as TypeScript or React
- Cloud certifications in AWS (e.g., Data Engineer or Solutions Architect)

Qualifications

- Bachelor’s degree in Computer Science, Engineering, or a related discipline
- Hands-on experience in data engineering or backend software engineering
- Strong programming skills in Python with experience in data processing frameworks such as Spark or PySpark
- Experience building and maintaining data pipelines and large-scale data systems (data lake or warehouse environments)
- Knowledge of SQL and experience working with structured and unstructured datasets
- Experience with distributed systems and scalable data architectures
- Familiarity with CI/CD practices, Agile methodologies, and application resiliency
- Experience with data tools such as dbt, Glue, or Trino
- Exposure to orchestration tools such as Airflow or Step Functions and experience with infrastructure as code
- Experience with containerization technologies such as Docker and Kubernetes
- Understanding of cloud platforms, preferably AWS
- Knowledge of streaming and real-time data processing (Kafka, Spark Streaming)
- Experience building automated testing frameworks across unit, integration, and end-to-end levels
- Exposure to modern frontend technologies such as TypeScript or React
- Cloud certifications in AWS (e.g., Data Engineer or Solutions Architect)

Responsibilities

- Build and maintain scalable data pipelines and architectures on cloud platforms, primarily AWS
- Develop, test, and deploy high-quality data and backend solutions with a focus on reliability and performance
- Design and implement microservices and APIs to support data workflows and integrations
- Optimize data processing systems to handle large-scale datasets efficiently and ensure high availability
- Implement data solutions using distributed processing frameworks such as Spark or PySpark
- Apply Agile and DevOps practices, including CI/CD, automation, and infrastructure as code
- Collaborate with stakeholders to understand data requirements and improve data accessibility and usability
- Ensure data quality, integrity, and consistency across systems and platforms
- Support integration of data into analytics and reporting platforms for business insights
- Contribute to building resilient, secure, and scalable systems aligned with engineering best practices

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