JP Morgan Chase

Runs a consumer bank and an investment bank

Data Engineer 3 - Multiple Teams

Data EngineeringConfirmed live in the last 24 hours

Full-timeMumbai, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

Formal training or certification in data engineering concepts and at least 3 years of applied experience.
Hands-on proficiency with Apache Spark or comparable distributed computing frameworks, including optimizing jobs for performance and cost at scale.
Production-level Databricks experience, including notebooks, Delta Lake, job orchestration, and cluster management.
Ability to write, debug, and optimize complex SQL queries against large and varied datasets.
Practical experience supporting ML model-serving infrastructure in production environments.
Familiarity with MLOps tools and practices, including model monitoring, versioning, and deployment automation.
A strong foundation in data architecture and data modeling for operational and analytical data stores.
Working knowledge of cloud data platforms such as AWS, Azure, or GCP, and proficiency with version control systems such as Git.
Hands-on exposure to distributed computing, cloud data platforms, and production ML infrastructure.
Hands-on experience with enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, and the ability to critically evaluate, validate, and refine generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; ability to guide peers on safe and effective use.

Responsibilities

Architect and maintain large-scale data pipelines using Apache Spark and related distributed computing frameworks to efficiently process structured and unstructured data.
Build and optimize batch and streaming data workloads for analytical and operational use cases.
Develop, optimize, and productionize notebooks, jobs, and workflows in Databricks for analytical and machine learning use cases.
Design and implement data architecture and data modeling strategies, including dimensional models, lakehouse structures, and schema design that balance performance, scalability, and governance.
Write and optimize complex SQL queries for data transformation, validation, and performance tuning across large datasets.
Support ML model-serving infrastructure so trained models are reliably deployed, monitored, and scaled in production.
Collaborate with data scientists and ML engineers on MLOps practices, including model versioning, CI/CD pipelines for ML artifacts, monitoring, and automated retraining workflows.
Partner with analytics, risk, and platform engineering stakeholders to translate business requirements into technical data solutions.
Maintain data quality, lineage, and security standards consistent with data governance and compliance expectations.
Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity across complex deliverables, validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve team effectiveness.
Apply knowledge of software development life-cycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Desired Qualifications

Experience with workflow orchestration tools such as Airflow or Databricks Workflows.
Familiarity with containerization technologies such as Docker and Kubernetes.
Exposure to CI/CD pipelines for data and ML workflows, and familiarity with feature stores and model registries such as MLflow.
Experience with real-time streaming architectures such as Kafka and Spark Structured Streaming.
Prior experience in financial services or other highly regulated industries, with an understanding of data governance and compliance requirements.

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Engineer III at JPMorganChase within the JPM - US Wealth Management Tech, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Architects and maintains large-scale data pipelines using Apache Spark and related distributed computing frameworks, ensuring efficient processing of structured and unstructured data.
Builds and optimizes both batch and streaming data workloads to support analytical and operational use cases.
Develops, optimizes, and productionizes notebooks, jobs, and workflows within the Databricks environment to support analytical and machine learning use cases.
Designs and implements data architecture and data modeling strategies, including dimensional models, lakehouse structures, and schema design that balance performance, scalability, and governance requirements.
Writes and optimizes complex SQL queries for data transformation, validation, and performance tuning across large datasets.
Supports ML model serving infrastructure, ensuring trained models are deployed, monitored, and scaled reliably in production environments.
Collaborates closely with data scientists and ML engineers on MLOps practices, including model versioning, CI/CD pipelines for ML artifacts, monitoring, and automated retraining workflows.
Partners with cross-functional stakeholders — including analytics, risk, and platform engineering teams — to translate business requirements into technical data solutions.
Maintains strong data quality, lineage, and security standards consistent with data governance and compliance expectations.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.

Required qualifications, capabilities, and skills

Formal training or certification on data engineering concepts and 3+ years applied experience.
Demonstrates strong, hands-on proficiency with Apache Spark or comparable distributed compute frameworks, including experience optimizing jobs for performance and cost at scale.
Holds direct, production-level experience with Databricks, including notebooks, Delta Lake, job orchestration, and cluster management.
Demonstrates solid command of SQL, with the ability to write, debug, and optimize complex queries against large and varied datasets.
Brings practical experience supporting ML model serving infrastructure in production environments.
Shows familiarity with MLOps tooling and practices, such as model monitoring, versioning, and deployment automation.
Possesses a strong foundation in data architecture and data modeling, covering both operational and analytical data store design.
Has working knowledge of cloud data platforms such as AWS, Azure, or GCP. Maintains proficiency with version control systems such as Git.
Demonstrates hands-on exposure to distributed computing, cloud data platforms, and production ML infrastructure.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred qualifications, capabilities, and skills

Experience with workflow orchestration tools such as Airflow or Databricks Workflows.
Familiarity with containerization technologies such as Docker and Kubernetes.
Exposure to CI/CD pipelines applied to data and ML workflows. Familiarity with feature stores and model registries such as MLflow.
Experience with real-time streaming architectures such as Kafka and Spark Structured Streaming.
Prior experience in financial services or other highly regulated industries, with an understanding of data governance and compliance requirements.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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