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

About the role

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.