Let's begin! Associate Data Engineer - Onsite Hybrid

Moody's•Boca Raton, FL

15h•Hybrid

About The Position

At Moody's, we unite the brightest minds to turn data into insight — we are seeking a Data Engineer to join our data platform organization and help enable the scalable delivery of high-quality data across the enterprise. 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, configurations, and operational processes Support data governance initiatives, including data quality, lineage, and access management. Our Data Estate Engineering team is mission-critical, powering one of the key and most complex components of Moody's Enterprise Data Platform — CORE. We are a modern, forward-thinking, innovation-driven data engineering team that supports the largest company and financials database in the world, serving as the backbone of Moody's success today and into the AI-driven future of the financial industry. By joining our team, you will contribute to high-impact data solutions at enterprise scale, collaborating with passionate engineers across global teams who are embracing AI-enabled ways of working, automation, and continuous innovation.

Requirements

2+ years of experience in data engineering, building and operating production data pipelines
Strong hands-on experience with Python, PySpark, Scala, and SQL
Proven experience designing or working with configuration-driven or metadata-driven data pipelines
Experience working in a Databricks-based data platform, with a 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

Nice To Haves

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

Responsibilities

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, configurations, and operational processes
Support data governance initiatives, including data quality, lineage, and access management