Senior Data Engineer

Responsibilities

• Lead the design and delivery of scalable, standardized data pipelines across multiple product teams
• 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

Requirements

• 6–9 years of experience in data engineering with a focus on scalable data platforms
• Strong proficiency in Python, including pandas, SQLAlchemy, and PySpark
• Hands-on experience with AWS Glue (ETL development, crawlers, and schema management)
• Experience with AWS Batch and Step Functions for workflow orchestration
• Expertise in Docker for containerized workloads
• Strong SQL skills and experience with relational databases like PostgreSQL and SQL Server
• Experience designing and managing S3-based data lakes (Parquet, JSON, and partitioning strategies)
• Bachelor’s degree in Computer Science, Engineering, or a related field

Preferred Qualifications

• Exposure to SageMaker or data quality tools
• Experience with Infrastructure as Code (CDK/Terraform)
• Interest in applying AI/LLMs within data workflows

About the Company

Moody's is a global leader in ratings and integrated risk assessment. We are transforming how the world sees risk by advancing AI to move from insight to action, helping our clients navigate uncertainty with clarity, speed, and confidence.