Data Engineer

About the role

Moody's is looking for a Data Engineer to help design, build, and optimise large‑scale data pipelines that power risk analytics and AI‑driven insights. You will work in a collaborative, cross‑functional environment, leveraging modern cloud technologies to transform raw data into actionable information.

Key responsibilities

• Design, develop, and optimise Spark‑based data pipelines for high‑volume processing.
• Implement parsing, normalisation, and standardisation logic across diverse datasets.
• Build and maintain ETL workflows ingesting data from Delta Lake, Parquet, and S3.
• Develop graph‑based analytics and entity‑resolution workflows.
• Contribute to data quality checks, validation, and incremental processing strategies.
• Write and maintain unit and integration tests using ScalaTest or JUnit.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field (or equivalent practical experience).
• Hands‑on experience with Apache Spark, preferably in Scala, with Python also acceptable.
• Experience working with Databricks in a cloud environment, preferably AWS.
• Strong understanding of distributed computing, data partitioning, and performance optimisation.
• Familiarity with modern data formats such as Parquet, Delta Lake, and JSON.
• Proficiency in SQL for data transformation and querying.

Required skills

• Apache Spark
• Scala
• Python
• Databricks
• AWS
• Parquet
• Delta Lake
• JSON
• SQL
• Git
• Gradle
• Maven
• SBT
• ScalaTest
• JUnit