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Data Engineering Lead
2 months ago
About the Role:
We are seeking a highly skilled and experienced Data Engineering Lead to join our team at Moody's Investors Service. As a key member of our data management team, you will be responsible for leading the development and implementation of our data pipelines, ensuring the efficient and scalable processing of large datasets.
Key Responsibilities:
- Data Extraction and Transformation: Design and implement data extraction and transformation processes to prepare data for use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
- Data Distribution: Develop and maintain data distribution platforms to make large datasets available across various distribution platforms, including SFTP, data marketplaces, and Databricks Delta Sharing.
- Bulk Data Feeds: Create and manage bulk data feeds in multiple formats, such as delimited text, JSON, Parquet, Avro, etc.
- Team Management: Lead a team of contractors and build a team of full-time engineers to support the development and implementation of our data pipelines.
- Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
- Optimization: Ensure data pipelines are efficient, scalable, and maintainable, and make recommendations for process improvements.
Requirements:
- Experience: 5-10 years of experience in data engineering or related fields.
- Programming Languages: Proficiency in Python and SQL.
- Data Formats: Experience with various data formats, including delimited text, JSON, Parquet, Avro, etc.
- Data Platforms: Familiarity with data distribution platforms, such as SFTP, data marketplaces, and Databricks.
- Data Engineering: Strong understanding of data extraction, transformation, and loading (ETL) processes.
- Team Leadership: Proven experience in managing a team of contractors and building a team of full-time engineers.
Preferred Qualifications:
- Experience with LLMs and RAGs: Understanding of how to prepare data for machine learning models and retrieval-augmented generation systems.
- Big Data Technologies: Experience with big data technologies and tools.
- Cloud Platforms: Familiarity with cloud platforms, such as AWS, Azure, or Google Cloud.
