AML Data Engineer

Job Description

Key Responsibilities:

Python Development:

• Proficient in Python, with strong knowledge of design patterns and both object-oriented and functional programming paradigms.
  
• Hands-on experience with modern IDEs such as VS Code, PyCharm, and tools like CoPilot or Cursor for efficient development and testing.
  
• Familiarity with searching and sorting algorithms, with the ability to apply them effectively in problem-solving.
  
• Experience working with multi-module and package-based project structures.
  
• Strong expertise in unit testing frameworks such as pytest and unittest, with a focus on writing robust and maintainable test cases.
  
• Proficient in managing virtual environments using tools like venv, conda, or pyenv to ensure isolated and reproducible development setups.
  

Big Data & Data Engineering:

• Proficient in extracting, transforming, and processing data from Teradata. (highly recommended)
  
• Strong SQL skills for querying, data manipulation, and optimization.
  
• Familiarity with the Hadoop ecosystem and its components, enabling efficient handling of large-scale data processing tasks.
  
• Hands-on experience with Apache Spark, with a preference for PySpark. (highly recommended)
  

Good to Have:

• Basic understanding of machine learning concepts and their applications.
  
• Exposure to XGBoost or similar ML frameworks.
  
• Experience with modeling techniques, including basic regression or classification tasks.
  

Required Qualifications & Skills:

• Educational Background: Bachelor's/master’s in related fields
  
• Experience: 3–10 years in data engineering
  
• Strong analytical, problem-solving, and communication skills.
  
• Strong programming skills in Python, SQL
  
• Proficiency in PySpark, Apache Spark, Hadoop, Teradata
  
• Advanced excel skills, excellent written and verbal communications, ability to manage multiple timelines and deliverables, knowledge of Model Risk Development Lifecycle (preferred).