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
- Proven ability to make critical technical design decisions and drive architectural direction for production data systems
- Demonstrated experience mentoring engineers and fostering technical growth within teams
- Strong focus on establishing and enforcing engineering best practices including code quality, testing, and documentation standards
- Effective communication skills for technical documentation, stakeholder management, and cross-functional collaboration
- Strong 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) for large-scale data processing
- Solid understanding of distributed computing, data partitioning, and performance optimisation
- Strong Spark SQL skills for data transformation, modelling, and optimisation
- Understanding of graph data structures, clustering techniques, and similarity approaches for entity resolution workflows
- Experience working with modern data formats such as Parquet, Delta Lake, and JSON
- Familiarity with statistical methods such as TF-IDF and similarity metrics in data processing contexts
- Proficiency with version control and build tools including Git, Gradle, Maven, or SBT
- Experience with testing frameworks such as ScalaTest or JUnit and test automation practices
- Ability to leverage AI-assisted development tools to improve productivity and code quality
- Strong analytical and problem-solving skills in complex data environments
Education
- Master’s degree in Computer Science, Information Technology, or a related field
- Equivalent practical experience will also be considered
Responsibilities
Design, build, and optimise large-scale data processing pipelines and graph-based data solutions, while providing technical guidance and mentoring to support high-quality engineering delivery
- Lead critical technical design decisions for data pipelines, graph architectures, and system scalability strategies
- Mentor and develop junior and mid-level engineers, providing technical coaching and career guidance
- Conduct architectural code reviews to ensure adherence to engineering best practices and design standards
- Define and enforce testing strategies across the team to maintain code quality and system reliability
- Design and maintain scalable Spark-based data pipelines for processing large volumes of data
- Develop graph-based analytics including clustering, similarity modelling, and entity resolution approaches
- Implement parsing, normalisation, and standardisation logic across complex datasets
- Build and maintain ETL workflows ingesting data from multiple sources such as Delta Lake, Parquet, and S3
- Implement data quality frameworks, validation checks, and incremental processing strategies
- Develop monitoring, diagnostics, and tooling to ensure pipeline reliability and performance
- Collaborate with Product, Data Science, and Engineering stakeholders to align technical roadmap with business objectives
- Document system architecture, data flows, and engineering decisions to support knowledge sharing
About the Team
Join our Data Engineering team, responsible for building scalable data pipelines across Change Data Capture, Entity Resolution, and Data Mastering
The team focuses on delivering high-performance, enterprise-grade data solutions that support critical business applications You will work on large-scale data challenges, contribute to technical innovation, and support the development of engineering capability through mentoring and collaboration . We embrace a more AI-enabled way of working, with a focus on automation, insight generation, and continuous innovation
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.