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Software Engineer III - PySpark/Databricks/Python/SQL

JPMorgan Chase

Software Engineer III - PySpark/Databricks/Python/SQL

JPMorgan Chase

Boston, MA, United States, US

·

On-site

·

Full-time

·

2w ago

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorgan Chase within the Commercial & Investment Bank, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

  • Executes software solutions, designs, develops, and troubleshoots software solutions, applying innovative thinking to solve complex technical challenges
  • Write secure, high-quality production code and maintain robust algorithms that integrate seamlessly with enterprise systems using Python, Java, Spark
  • Produce architecture and design artifacts for complex applications, ensuring all design constraints are met throughout software development
  • Gather, analyze, and synthesize large, diverse data sets to develop visualizations and reporting to enable data-driven decision-making
  • Proactively identify hidden issues and patterns in data, using insights to enhance coding practices and system architecture
  • Design and implement robust data ingestion & curation pipelines to bring diverse datasets into the cloud / Databricks
  • Contribute to software engineering communities of practice and participate in events exploring new and emerging technologies
  • Foster a team culture of diversity, equity, inclusion, and respect

Required qualifications, capabilities, and skills

  • Formal training or certification on Software Engineering concepts and 3+ years applied experience
  • Practical experience in system design, application development, testing, and ensuring operational stability
  • Strong in one or more programming languages including Python.

Spark and Java:

  • Strong SQL skills
  • Experience developing, debugging, and maintaining code in a large corporate environment, using modern programming and database querying languages
  • Comprehensive understanding of the Software Development Life Cycle (SDLC)
  • Solid grasp of agile methodologies, including CI/CD, application resiliency, and security best practices
  • Demonstrated expertise in software applications and technical processes within disciplines such as data platforms, cloud, Agentic AI frameworks and AI/ML

Preferred qualifications, capabilities, and skills

  • Experience in data engineering, with a strong understanding of data modeling and ETL processes
  • Working knowledge of Databricks
  • Familiarity with CockroachDB, Apache Iceberg, Trino, and AWS
  • Experience with Spring frameworks, Kafka, and RESTful APIs

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About JPMorgan Chase

JPMorgan Chase

JPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.

300,000+

Employees

New York City

Headquarters

Reviews

4.2

10 reviews

Work Life Balance

4.2

Compensation

4.3

Culture

4.5

Career

4.4

Management

4.1

75%

Recommend to a Friend

Pros

Good pay and benefits

Work-life balance

Career advancement opportunities

Cons

Heavy workload at times

Career advancement takes time

Pay could be better in some roles

Salary Ranges

47 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analyst

21 reports

$126,500

total / year

Base

$110,000

Stock

-

Bonus

-

$95,450

$155,250

Interview Experience

4 interviews

Difficulty

2.8

/ 5

Duration

14-28 weeks

Interview Process

1

Application Review

2

HireVue Video Interview

3

Technical/Behavioral Assessment

4

Final Interview Round

5

Offer Decision

Common Questions

Behavioral/STAR

Technical Knowledge

Past Experience

Culture Fit

Case Study