Data Engineer
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Before you apply
LocationBrussels, Belgium
Typefull-time
SeniorityMid
DepartmentEngineering
Company size10,000+ people
PostedAug 20, 2026 · 2mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
Skills
Apache Spark
Scala
Python
Databricks
AWS
Parquet
Delta Lake
JSON
SQL
Git
Gradle
Maven
SBT
ScalaTest
JUnit
About the company
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
•
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
Education
•
Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field
•
Equivalent practical experience will also be considered
Responsibilities
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.The team is embracing a more AI-enabled way of working, with a focus on automation, insight generation, and continuous innovation
Legal
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.