Software Engineer III - AWS, Databricks, Python

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 120,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Lead Data Engineer in its Corporate Technology - FINTECH team in Columbus, OH. You will contribute to agile data pipelines, data models, and analytics solutions across multiple business functions.

Responsibilities include building secure, scalable data platforms, implementing backup and archiving, and ensuring data security with proper access controls. Strong Python, AWS, and Databricks experience is essential.

Qualifications

• Formal training or certification on software engineering concepts and 3+ years applied experience.
• 3+ years of experience using Databricks, PySpark, AWS; creating ETL pipelines from scratch is a must.
• 3+ years of experience working with AWS services (Lambda, Step Functions, SQS, SNS, API Gateway, Secrets Manager, storage) is a must.
• 3+ years of experience in software engineering and object-oriented programming with Python and Terraform.
• Hands-on with open-source frameworks/libraries such as Apache NiFi, Apache Airflow and Autosys.
• Strong understanding of REST API development using FASTAPI or equivalent frameworks.
• Advanced SQL skills (joins and aggregations).

Responsibilities

• Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms.
• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way.
• Implements database backup, recovery, and archiving strategy.
• Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision.
• Adds to team culture of diversity, opportunity, inclusion, and respect.

Skills

Databricks

PySpark

AWS

Python

Terraform

REST API

SQL

Tools

Apache NiFi

Apache Airflow

Autosys

Jenkins

Jira

Git

Job description

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer at JPMorgan Chase within the Corporate Technology - FINTECH team, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms
• Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
• Implements database back-up,recovery, and archivingstrategy
• Evaluates and reports onaccesscontrol processes todetermineeffectiveness of dataasset security with minimal supervision
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• 3+ years of experience using technologies such as Databricks , Pyspark, AWS, is essential and creating ETL Pipeline from scratch is a must.
• 3+ years of experience working with AWS (Lambda, Step Function, SQS, SNS, API Gateway, secrets manager and storage services) is a must.
• 3+ years of experience in software engineering and object-oriented programming skills with expertise in Python and Terraform
• Hands on experience with open-source frameworks/libraries, such as Apache NiFi, Apache Airflow and Autosys.
• Strong understanding of REST API development using FASTAPI or equivalent frameworks.
• Advanced at SQL (e.g., joins and aggregations)

Preferred qualifications, capabilities, and skills

• Familiar with development tools such as Jenkins, Jira, Git/Stash, spinnaker
• Familiarity with unit testing frameworks such as pytest or unittest.
• Extensive experience in statistical data analysis, with the ability to select appropriate tools and identify data patterns for effective analysis, as well as experience throughout the data lifecycle