Software Engineer III- Data Engineer; Python, Databricks and AWS

As a Data Engineer III at JPMorgan

Chase 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.

• 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.

• 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.

• 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.