The TBE Data Engineering (DE) team builds and operates trusted, governed data products and pipelines that enable analytics, reporting, and downstream applications across TBE. The team partners with product, analytics, architecture, risk, and finance to deliver scalable cloud data solutions, improve data quality and observability.

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

A Data Engineer II should possess strong hands-on experience in building and managing scalable data pipelines using modern data engineering tools and technologies such as Python, PySpark, SQL, and GCP/AWS/Azure. The candidate should collaborate with cross-functional teams to ensure alignment with business needs and enterprise standards and have a solid understanding of data processing frameworks, performance optimization, and data quality practices.
Responsibilities:
 

• Design, build, and optimize scalable data pipelines using Python/ PySpark/ sql

  
  

• Develop and maintain ETL/ELT workflows on GCP/AWS/Azure

  
  

• Work with GCP services such as BigQuery/Snowflake, Cloud Storage and Dataproc/EMR/Databricks etc

  
  

• Ensure data quality, integrity, and performance across data systems

  
  

• Collaborate with cross-functional teams to understand data requirements and deliver solutions

  
  

• Monitor, troubleshoot, and optimize data workflows

  
  

• Implement best practices for data engineering, including code quality, testing, and documentation

  
  

• Bachelor's degree in Computer Science, Computer Engineering, and/or comparable experience; advanced degree preferred

  
  

• Strong experience in Python and PySpark

  
  

• Hands-on experience with Google Cloud Platform (GCP)/AWS/Azure

  
  

• Good understanding of data warehousing concepts and data modeling

  
  

• Familiarity with SQL and performance tuning

  
  

• Knowledge of version control tools like Git

  
  

• 2+ years of hands-on experience in data engineering, with a strong focus on building and maintaining scalable data pipelines.

  
  

• Proven experience working with Python, PySpark, and SQL for data processing, transformation, and analysis.

  
  

• Hands-on experience with Google Cloud Platform (GCP) or any Equivalent Cloud services such as BigQuery/Snowflake, Cloud Storage, and Dataproc/Databricks/EMR, Airflow/Composer/Astronomer etc.

  
  

• Experience in designing and implementing ETL/ELT workflows in a distributed data processing environment.

  
  

• Strong experience in writing optimized SQL queries and performance tuning for large datasets.

  
  

• Experience in handling large-scale data processing and working with distributed computing frameworks.

  
  

• Exposure to data modeling, data warehousing concepts, and schema design.

  
  

• Experience with workflow orchestration tools such as Apache Airflow (or similar).

  
  

• Familiarity with version control systems like Git and CI/CD practices.

  
  

• Strong troubleshooting and debugging skills in data pipelines and production environments.