Senior Software Engineer III, Databricks/Spark/AWS Data Engineer

JPMorgan Chase

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Quick summary

Work type: On-site
Location: OH
Employment: Full-time
Posted: 2 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $183k

$135k most similar roles pay here $222k

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1174 open roles on FindRole.

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At a glance

TL;DR · Senior Software Engineer III, Databricks/Spark/AWS Data Engineer

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Software Engineer III - Senior Databricks/Spark/AWS Data Engineer joins the Employee and Experience Technology team to design and deliver secure, scalable technology products. This role focuses on modernizing a Databricks-on-AWS lakehouse by building new data pipelines using Apache Spark and PySpark. Key responsibilities include developing high-quality production code, optimizing cluster performance for cost efficiency, and implementing medallion architecture patterns with Delta Lake and Delta Live Tables. The engineer will also manage monitoring via AWS CloudWatch, automate workflows, and utilize AI-assisted tools like Claude or GitHub Copilot to enhance productivity. Technical requirements include expertise in the AWS ecosystem—including S3, EMR, Glue, Lambda, and Athena—alongside Python, SQL, and CI/CD pipelines. The role involves delivering curated datasets for BI partners using Sigma, Tableau, and Alteryx within a complex financial services domain.

Skills

Databricks PySpark Apache Spark AWS Delta Lake Python SQL S3 EMR Glue Lambda Athena Parquet Iceberg CI/CD Git Bitbucket Jenkins Airflow Tableau Sigma Alteryx

What you'll do

Design, build, and maintain production data pipelines on Databricks using PySpark.
Optimize PySpark jobs and Databricks clusters for performance, scalability, and cost efficiency.
Implement scalable data frameworks using medallion lakehouse patterns for workforce data analytics.
Develop data quality checks and validation processes using Delta Lake and Delta Live Tables.
Implement monitoring and alerting systems using Databricks and AWS CloudWatch to ensure operational stability.
Automate the remediation of recurring issues using Databricks Workflows and AWS-native tools.
Utilize AI and Agentic AI solutions to accelerate data pipeline development and improve code quality.
Deliver curated, reliable datasets to business partners for use in Sigma, Tableau, and Alteryx.

What we're looking for

Formal training or certification in software engineering concepts with 3+ years of applied experience in data engineering.
Advanced, hands-on expertise in Apache Spark (PySpark) for large-scale distributed data processing on Databricks.
Strong expertise across the AWS data ecosystem including S3, EMR, Glue, Lambda, and Athena.
Strong programming skills in Python for data processing and application development.
Proficiency in automation and continuous delivery methods using CI/CD pipelines with tools like Git, Jenkins, or Spinnaker.
Practical experience in system design, application development, testing, and operational stability within agile methodologies.
In-depth knowledge of the financial services industry and their IT systems.
Solid SQL and data modeling skills (experience with relational databases such as Oracle is preferred); Databricks certifications are preferred.

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