Software Engineer III- Data Engineer(Python, Databricks and AWS)

JPMorgan Chase & Co.; Plano (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Data Engineer III to design and build trusted data pipelines and analytics solutions in a secure, scalable environment within the Corporate Sector. You will work on end-to-end data engineering, collaborating with cross-functional teams to deliver scalable data products and insights.

You will leverage Databricks, Apache Spark, AWS, Airflow, and Kafka, ensure data quality and security, and contribute to AI initiatives across the data lifecycle.

Qualifications

• Formal training or certification in data engineering concepts with 3+ years of hands-on experience.
• Experience in the full lifecycle of data engineering, from ingestion to production.
• Demonstrated experience using enterprise‑authorized AI capabilities to support data engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted outputs before use, escalating when uncertain and following data handling requirements.
• Strong hands-on expertise with Databricks, Spark, AWS (S3, Redshift, Lambda), Airflow, and Kafka.
• Proficiency in Python and SQL for data manipulation and analysis.
• Experience with data modeling, ETL development, and data warehousing.
• Familiarity with AI/ML workflows and supporting data pipelines for machine learning.
• Certifications in AWS, Databricks, Spark, etc. are desirable.
• Excellent problem-solving skills and attention to detail.
• Strong communication and teamwork abilities.

Responsibilities

• Design, develop, and optimize end-to-end data pipelines using Databricks, Apache Spark, and AWS cloud services.
• Implement and manage workflow orchestration with Airflow.
• Integrate and process streaming data using Kafka.
• Use AI capabilities to accelerate data pipeline design analysis, ensuring data handling security and traceability.
• Collaborate with data scientists, analysts, and stakeholders to understand requirements and deliver data solutions.
• Ensure data quality, integrity, and security across the data lifecycle.
• Develop and maintain ETL processes, data models, and data warehouses.
• Write Python and SQL for data processing and analysis.
• Apply AI-assisted SDLC quality routines to data pipelines for validation and auditability.
• Support AI/ML initiatives by preparing data for model development and deployment.
• Monitor, troubleshoot, and optimize data workflows for performance and reliability.
• Stay current with best practices in data engineering and AI.

Skills

Databricks

Apache Spark

AWS

Python

SQL

Airflow

Kafka

Data modeling

ETL development

Data warehouses

Tools

Docker

Kubernetes

Airflow

Redshift

Job description

Job Description:

As a Data Engineer III at JPMorganChase within the Corporate Sector you 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. As a Data Engineer, you will be responsible for designing, building, and maintaining robust data pipelines and architectures that support the entire data engineering lifecycle. You will work closely with cross-functional teams to deliver scalable solutions that drive business insights and enable advanced analytics, including AI initiatives.

Key Responsibilities:

• Design, develop, and optimize end-to-end data pipelines using Databricks, Apache Spark, and AWS cloud services.


• Implement and manage workflow orchestration with Airflow.


• Integrate and process streaming data using Kafka.


• 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.


• Collaborate with data scientists, analysts, and business stakeholders to understand requirements and deliver high-quality data solutions.


• Ensure data quality, integrity, and security across all stages of the data lifecycle.


• Develop and maintain ETL processes, data models, and data warehouses.


• Write efficient, maintainable code in Python and SQL for data processing and analysis.


• 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.


• Support AI and machine learning initiatives by preparing and engineering data for model development and deployment.


• Monitor, troubleshoot, and optimize data workflows for performance and reliability.


• Stay current with industry best practices and emerging technologies in data engineering and AI.

Required qualifications, capabilities, and skills

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


• Proven experience in the full lifecycle of data engineering, from ingestion to production.


• 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.


• Strong hands-on expertise with Databricks, Apache Spark, AWS (S3, Redshift, Lambda, etc.), Airflow, and Kafka.


• Proficient in Python programming and SQL for data manipulation and analysis.


• Experience with data modeling, ETL development, and data warehousing.


• Familiarity with AI/ML workflows and supporting data pipelines for machine learning.


• Certifications in relevant technologies (AWS, Databricks, Spark, etc.) are highly desirable.


• Excellent problem-solving skills and attention to detail.


• Strong communication and teamwork abilities.

Preferred qualifications, capabilities, and skills

• Experience with containerization (Docker, Kubernetes).


• Knowledge of data governance and compliance standards.


• Exposure to big data tools and frameworks.


• Ability to work in a fast-paced, agile environment.