Software Engineer lll - Senior Databricks/Spark/AWS Data Engineer

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

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 JPMorganChase within the Employee and Experience Technology team, 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. In this role, you will help drive our modernization to a Databricks-on-AWS lakehouse, building all new data pipelines using Apache Spark (PySpark) on Databricks.

Job Responsibilities

• Design, build, and maintain new data pipelines on Databricks using PySpark, developing secure, high-quality production code and reviewing and debugging processes implemented by others.

• Optimize and tune PySpark jobs and Databricks clusters for performance, scalability, and cost efficiency (partitioning, caching, and resource management).

• Design and implement scalable data frameworks to manage end-to-end Databricks pipelines for workforce data analytics, applying medallion (bronze/silver/gold) lakehouse patterns.

• Implement data quality checks and validation processes — using Delta Lake and Delta Live Tables expectations — to ensure accuracy and reliability of data.

• Implement robust monitoring and alerting to proactively address data ingestion issues, leveraging Databricks and AWS CloudWatch to optimize performance and throughput.

• Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability, using Databricks Workflows and AWS-native automation.

• Leverage AI and Agentic AI solutions to accelerate data pipeline development, and adopt AI-assisted engineering tools (e.g., Claude, GitHub Copilot) to improve developer productivity and code quality.

• Provision and deliver curated, reliable data sets to our BI partners (who work in Sigma, Tableau, and Alteryx), enabling their reporting and analytics use cases.

• Work with business stakeholders to understand requirements and design appropriate solutions, producing architecture and design artifacts for complex applications.

• Contribute to software engineering communities of practice that explore new and emerging technologies, fostering a culture of diversity, opportunity, inclusion, and respect.

Required Qualifications, Capabilities & Skills

• Formal training or certification in software engineering concepts with 3+ years of applied experience in data engineering, including design, application development, testing, and operational stability.

• Advanced, hands-on expertise in Apache Spark (PySpark) for large-scale distributed data processing, with strong proficiency building and operating production pipelines on Databricks (Delta Lake and lakehouse patterns).

• Strong expertise across the AWS data ecosystem, including S3, EMR, Glue, Lambda, and Athena, along with AWS storage and compute services; experience with data formats such as Parquet and Iceberg.

• Strong programming skills in Python for data processing and application development (Java or Scala a plus).

• Proficiency in automation and continuous delivery methods, utilizing CI/CD pipelines with tools like Git/Bitbucket, Jenkins, or Spinnaker for automated deployment and version control.

• Hands-on practical experience delivering system design, application development, testing, and operational stability, with advanced understanding of agile methodologies, application resiliency, and security.

• In-depth knowledge of the financial services industry and their IT systems.

• Solid SQL and data modeling skills for efficient data management and retrieval (experience with relational databases such as Oracle a plus).

• Experience with scheduling tools like Airflow and Autosys to automate and manage job scheduling for efficient workflow execution.

Preferred Qualifications, Capabilities & Skills

• Databricks certifications (e.g., Databricks Certified Data Engineer Associate/Professional).

• Familiarity with Generative AI and Agentic AI frameworks, including experience with AI coding assistants such as Claude and GitHub Copilot in an engineering workflow.

• Deeper expertise in the AWS cloud platform and its broader service catalog.