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

JPMorgan Chase
Plano, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 2 days ago
airflowawsdatabricksdockerkafkakubernetes

Full Job Posting

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.

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