Data Engineer

Moody's Corporation is a global leader in ratings and integrated risk assessment, focused on transforming how the world sees risk. The Data Engineer role involves developing and maintaining data pipelines, collaborating with cross-functional teams, and applying data security best practices to support analytics and machine learning workloads.

Responsibilities:

• Develop and maintain data pipelines on the Databricks Lakehouse, contributing to governed data products that support compliant, downstream analytics
• Develop and maintain data pipelines that provide access to usable data for Corps and Gov business applications, embedded analytics, and ad-hoc stakeholder requests
• Contribute to the “pipes” that enable downstream use cases—ingestion, transformation, and delivery patterns supporting customer-facing risk solutions
• Build and support Delta Live Tables (DLT) pipelines under guidance from senior engineers to improve data freshness, quality, and resilience
• Write and test transformations using dbt, SQL, and Spark/Python; participate in code reviews and follow established quality standards
• Help orchestrate and monitor workflows in AWS MWAA (Airflow) and Databricks Workflows, including alerting setup, retry logic, and backfill procedures
• Support infrastructure and reusable frameworks that allow stakeholders to create analytics and ML workloads on top of platform data
• Collaborate with cross-functional stakeholders—including KYC/AML experts, product managers, and application developers—to understand data needs and deliver solutions
• Apply data security best practices (access controls, encryption, secrets management) and observability patterns (monitoring, data quality checks, alerting) in day-to-day work
• Participate in incident triage and pipeline troubleshooting, learning to diagnose root causes and implement durable fixes
• Proactively learn and adopt team standards for code quality, testing, documentation, and operational readiness

Requirements:

• 1–3 years of experience in data engineering, backend engineering, or a related technical role
• Foundational experience working with the Databricks Lakehouse (Delta Lake) or a comparable platform (Snowflake, BigQuery); exposure to Delta Live Tables (DLT) is a plus
• Familiarity with orchestration tools such as AWS MWAA (Managed Workflows for Apache Airflow) or Databricks Workflows
• Solid SQL skills and working knowledge of Python; exposure to Spark and dbt is preferred
• Understanding of DataOps principles: CI/CD for data pipelines, automated testing, and environment promotion practices
• Awareness of data security fundamentals—encryption at rest and in transit, role-based access control, secrets management—and a willingness to deepen that knowledge
• Understanding of data observability and monitoring concepts (pipeline health, data quality checks, alerting, and incident triage)
• Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency. Interest in exploring AI systems and a willingness to develop awareness of responsible AI practices, including risk management and ethical use
• Bachelor's degree in Computer Science, Engineering, or a related field required
• Exposure to Delta Live Tables (DLT) is a plus
• Exposure to Machine Learning concepts (e.g., feature generation, training vs. serving) is preferred; comfort collaborating with data scientists and ML engineers
• Master's degree in a related discipline is preferred