At Moody's, we unite the brightest minds to turn today's risks into tomorrow's opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are-with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody's is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we're advancing AI to move from insight to action-enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.

Skills and Competencies

• 6-9 years of experience in data engineering with a strong focus on scalable data platforms
• Strong proficiency in Python including pandas, SQLAlchemy, and PySpark
• Hands-on experience with AWS Glue including ETL development, crawlers, and schema management
• Experience working with AWS Batch and Step Functions for workflow orchestration
• Expertise in Docker for containerized workloads
• Strong SQL skills and experience with relational databases such as PostgreSQL and SQL Server
• Experience designing and managing S3-based data lakes, including formats such as Parquet and JSON and partitioning strategies
• Ability to define engineering patterns, create documentation, and mentor team members
• Exposure to SageMaker, data quality tools, or Infrastructure as Code (CDK/Terraform) is a plus
• Interest in applying AI/LLMs within data workflows

Education

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Responsibilities
Lead the design and delivery of scalable, standardized data pipelines across multiple product teams while driving best practices in data engineering.

• Own end-to-end data pipeline architecture including ingestion, transformation, and productionisation
• Build, maintain, and optimize AWS Glue ETL jobs and manage schema evolution
• Orchestrate data workflows using AWS Batch and Step Functions
• Develop reusable pipeline patterns, frameworks, and templates to improve scalability and efficiency
• Partner with data science teams to support model deployment and operationalization
• Containerize data workloads using Docker for consistency and portability
• Establish data quality, validation, and monitoring practices across pipelines
• Mentor engineers and promote best practices in data engineering and platform design

About the Team
The team operates in a multi-squad environment focused on building scalable data platforms and pipelines. There is a strong emphasis on standardization, cross-team collaboration, and delivering high-quality, reliable data solutions that support a wide range of business and product initiatives.

Moody's is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody's Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.