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  3. Data Engineer III - Databricks, Pyspark, Python, AWS
JPMorganChase
JPMorganChase

Data Engineer III - Databricks, Pyspark, Python, AWS

Bengaluru, Karnataka, India
3+ years exp
Full-time
Posted 2d ago
0 views
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Job Description

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

As a Data Engineer III - Databricks, Pyspark, Python, AWS at JPMorgan Chase within the Commercial & Investment Bank, you'll 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

  • Design, develop, and maintain

big data pipelines

(

batch

and

streaming

) using

PySpark/Spark

and

Databricks

.

  • Lead/own

data modelling

and

solution design

for data products, including defining target-state architecture, data flows, and transformation patterns.

  • Build scalable ingestion and transformation workflows for

high-volume datasets

, ensuring

reliability

,

quality

, and

performance

.

  • Develop and optimize complex

SQL

transformations, reconciliation queries, and analytical datasets; perform

query tuning

for large-scale workloads.

  • Apply strong

data warehousing concepts

(

dimensional modeling

,

SCDs

,

partitioning strategies

, etc.) to build well-structured, analytics-ready data layers and Leverage common

AWS services

, with strong emphasis on

S3

and AWS

data processing capabilities

, to support scalable storage and processing.

  • Perform advanced

debugging

and

troubleshooting

across distributed

Spark

workloads (

data skew

,

shuffle tuning

,

memory/compute optimization

).

  • Implement engineering best practices:

modular design

,

efficient coding

,

code reviews

, and

CI-friendly

development approaches.

  • Use

GitHub/Bitbucket

and standard

version control

workflows to manage codebase, peer reviews, and releases.

  • Partner with

cross-functional stakeholders

to convert requirements into robust

big data solutions

.

  • Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
  • Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.

 

Required qualifications, capabilities, and skills

 - Formal training or certification on data engineering concepts and 3+ years applied experience

  •  Experience in

data engineering / big data engineering

, with strong hands-on delivery and Expert-level

SQL

skills (must be extremely strong): complex joins,

window functions

,

CTEs

, optimization, and

analytical problem solving

at scale.

  • Strong hands-on coding experience with

Python

in production environments; demonstrated ability to write

efficient

,

maintainable

code.

  • Deep expertise in

Apache Spark (in depth)

and

PySpark

, including

performance tuning

and

distributed processing fundamentals

.

  • Strong experience with

Databricks

for large-scale data processing and pipeline development.

  • Strong understanding of

data warehousing concepts

and best practices; proven capability in

data modelling

and

solution design

for scalable, maintainable data platforms/products.

  • Experience implementing both

batch

and

streaming

data processing solutions.

  • Familiarity with

AWS S3

and common

AWS services

used in data platforms and processing

  • Excellent

debugging

,

troubleshooting

,

problem-solving

skills and experience with

GitHub

,

Bitbucket

, and

version control

best practices.

  • Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
  • Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.

 

Preferred qualifications, capabilities, and skills

 - Good to have: infrastructure provisioning in

AWS

using

Infrastructure as Code (IaC)

(e.g.,

Terraform

,

AWS CloudFormation

).

Benefits & Perks

visioning in

AWS

using

Infrastructure as Code (IaC)

(e.g.,

Terraform

,

AWS CloudFormation

).

Frequently Asked Questions

How to apply for Data Engineer III - Databricks, Pyspark, Python, AWS at JPMorganChase?

Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.

What is the salary for this role?

Salary details will be discussed during the interview.

What experience is required?

3+ years of experience is required.

Is this position still open?

Yes, currently active and accepting applications.

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JPMorganChase

JPMorganChase

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Data Engineer III - Databricks, Pyspark, Python, AWS

JPMorganChase · Bengaluru

Apply on Company Website