Data Engineer
Role details
Job location
Tech stack
Job description
Support the design, development, and optimisation of data pipelines and data processing solutions within a scalable data engineering environment
- Support the design and development of Spark-based data pipelines for processing large volumes of data
- Implement parsing, normalisation, and standardisation logic across 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 support reliability
- Support monitoring and troubleshooting of data pipelines
- Collaborate with cross-functional teams including Product, Data Science, and Engineering
- Document data flows and technical solutions
- Participate in code reviews and contribute to engineering best practices
About the Team
Join our Data Engineering team, responsible for building scalable data pipelines across Change Data Capture, Entity Resolution, and Data Mastering You will work on large-scale data challenges in a collaborative environment, contributing to high-quality data solutions and developing your technical capabilities
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.
Requirements
Do you have experience in Spark?, Do you have a Master's degree?, * Experience building scalable data applications using Apache Spark, with proficiency in Scala preferred and Python also acceptable
- Hands-on experience working with Databricks in a cloud environment (AWS preferred)
- Good understanding of distributed computing, data partitioning, and performance optimisation
- Experience working with modern data formats such as Parquet, Delta Lake, and JSON
- Strong SQL skills for data transformation and querying
- Understanding of graph data structures, clustering techniques, and similarity approaches
- Familiarity with statistical methods such as TF-IDF and similarity metrics
- Proficiency with version control and build tools including Git, Gradle, Maven, or SBT
- Awareness of code quality, maintainability, and testing practices using frameworks such as ScalaTest or JUnit
- Experience contributing to testing approaches including unit and integration testing
- Ability to use AI-assisted development tools to improve productivity and support development tasks
- Strong analytical and problem-solving skills
- Effective communication skills and ability to collaborate within cross-functional teams
- Willingness to learn new technologies and develop engineering expertise, * Bachelor's or Master's degree in Computer Science, Information Technology, or a related field
- Equivalent practical experience will also be considered