Data Engineer

Responsibilities

• Support the design, development, and optimization of Spark-based data pipelines for processing large volumes of data
• Implement parsing, normalization, and standardization logic across various datasets
• Assist in developing graph-based analytics and entity resolution workflows
• Build and maintain ETL workflows ingesting data from sources such as Delta Lake, Parquet, and S3
• Contribute to data quality checks, validation, and incremental processing approaches
• Write and maintain unit and integration tests to ensure system reliability
• Collaborate with cross-functional teams including Product, Data Science, and Engineering
• Participate in code reviews and contribute to engineering best practices

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field
• Experience building scalable data applications using Apache Spark (Scala preferred, Python acceptable)
• Hands-on experience with Databricks in a cloud environment (AWS preferred)
• Strong SQL skills for data transformation and querying
• Proficiency with version control tools such as Git
• Solid understanding of distributed computing, data partitioning, and performance optimization
• Experience with modern data formats like Parquet, Delta Lake, and JSON
• Familiarity with testing frameworks such as ScalaTest or JUnit

Preferred Qualifications

• Understanding of graph data structures, clustering techniques, and similarity approaches
• Familiarity with statistical methods such as TF-IDF
• Experience with build tools including Gradle, Maven, or SBT
• Ability to use AI-assisted development tools to improve productivity

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.