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

JPMorganChase • Plano, TX

1d

About The Position

As a Data Engineer III at JPMorganChase within the Corporate Sector, you serve as a seasoned member of an agile team designing and delivering trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for designing, building, and maintaining robust data pipelines and architectures that support the full data engineering lifecycle. You work with cross-functional teams to deliver scalable solutions that drive business insights and enable advanced analytics, including AI initiatives.

The role involves developing and optimizing data pipelines and workflows, supporting AI and machine learning initiatives, and ensuring data quality, integrity, security, resiliency, and traceability. It also includes using enterprise-authorized AI capabilities to support data engineering work, while validating outputs and handling data according to sensitivity and security requirements.

Requirements

• Formal training or certification in data engineering concepts and at least 3 years of applied experience.
• Proven experience across the full data engineering lifecycle, from ingestion to production.
• Demonstrated experience using enterprise-authorized AI capabilities in 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, such as query suggestions, test ideas, or model change summaries, before use; escalate when uncertain and follow data handling requirements.
• Strong hands-on expertise with Databricks, Apache Spark, AWS services (such as S3, Redshift, and Lambda), Airflow, and Kafka.
• Proficiency 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.
• Excellent problem-solving skills and attention to detail.
• Strong communication and teamwork abilities.

Nice To Haves

• Certifications in relevant technologies such as AWS, Databricks, or Spark.
• Experience with containerization technologies such as Docker and Kubernetes.
• Knowledge of data governance and compliance standards.
• Exposure to big data tools and frameworks.
• Ability to work in a fast-paced, agile environment.

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 enterprise-authorized AI capabilities to accelerate data pipeline and 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 Python and SQL code for data processing and analysis.
• Apply reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines, such as test generation and control validation, ensuring traceability, auditability, and alignment with resiliency and security expectations.
• Prepare and engineer data for AI and machine learning 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed