at J.P. Morgan
Bulge Bracket Investment BanksPosted 15 days ago
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**Software Engineer III - Data Engineer (Python, Databricks, AWS)** Design and deliver scalable data solutions at JPMorganChase. As a senior team member, you will: - Engineer end-to-end data pipelines using Databricks, Spark, AWS, and Kafka. - Orchestrate workflows with Airflow and maintain ETL processes. - Collaborate cross-functionally to deliver high-quality data solutions, ensuring data quality, security, and integrity. - Apply AI-assisted practices to strengthen SDLC and support AI/ML initiatives. - Monitor, troubleshoot, and optimize data workflows. Requirements: 3+ years of data engineering experience, proficiency in Python and SQL, hands-on expertise with listed technologies, and a strong problem-solving attitude. Certification in relevant technologies is a plus. Thrives in an agile, fast-paced environment.
- Compensation
- Not specified
- City
- Not specified
- Country
- United States
Currency: Not specified
Full Job Description
Location: Plano, TX, United States
Job Description:
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.
- 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.
