Data Engineer-Data Platform & Analytics

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.

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.

• Onsite/hybrid in Boca Raton, FL
• 5+ years of experience in data engineering, building and operating production data pipelines
• Strong hands‑on experience with:
  • Python
  • Py Spark
  • Scala
  • SQL
• Proven experience designing or working with configuration‑driven or metadata‑driven data pipelines
• Experience working in a Databricks‑based data platform
• Solid understanding of data modeling, schema evolution, and large‑scale dataset management
• Experience deploying data solutions in AWS and Azure cloud environments
• Working knowledge of CI/CD concepts and experience integrating data pipelines into automated delivery workflows
• Strong collaboration and communication skills, with the ability to work across global, cross‑functional teams
• Demonstrated proficiency in artificial intelligence concepts, with hands‑on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI‑powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use

• Experience using dbt for data transformations, testing, and documentation
• Familiarity with data governance, cataloging, and lineage tools
• Experience supporting externally facing or commercial data products
• Exposure to infrastructure‑as‑code or platform automation tooling
• Knowledge of data quality frameworks and monitoring solutions

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

In this role, you will be responsible for implementing robust ETL pipelines that make a large and diverse set of data domain datasets available within our Databricks ecosystem. These datasets support internal analytical applications as well as commercial data feed products consumed by external clients, making reliability, scalability, and data quality essential. This position emphasizes configuration‑driven data pipeline development using Python, PySpark, Scala and SQL, allowing efficient onboarding and management of a growing number of datasets across multiple domains.

• Develop, and maintain scalable ETL/ELT pipelines that ingest, transform, and publish datasets across multiple data domains
• Implement and extend configuration‑based pipeline frameworks to efficiently support numerous datasets with consistent patterns and controls
• Build data transformations and validations using Python, PySpark, and SQL within Databricks
• Ensure data products are well‑structured, performant, and optimized for downstream consumption by internal teams and external commercial clients
• Partner with data producers, platform teams, and consumers to define data contracts, schemas, SLAs, and quality standards
• Apply best practices for data reliability, observability, performance tuning, and cost optimization
• Contribute to and follow CI/CD practices for data pipelines, including automated testing, deployment, and promotion across environments
• Develop and operate data pipelines in cloud environments, with hands‑on experience across AWS and Azure
• Produce and maintain clear technical documentation for pipelines,…