Data Engineering Lead
4 weeks ago
We are seeking a dynamic and experienced Data Engineering Lead to spearhead our efforts in transforming and optimizing our extensive data assets for use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. This pivotal role involves extracting and transforming entity-level data, distributing large datasets across various platforms, and managing bulk data feeds in multiple formats. The successful candidate will lead a team of contractors and full-time engineers, collaborate with data scientists and analysts, and ensure the efficiency, scalability, and maintainability of our data pipelines. With a strong background in data engineering, proficiency in Python and SQL, and experience with diverse data formats and platforms, the ideal candidate will drive innovation and excellence in our data management processes.
Key Responsibilities:- Extract and transform entity-level data into formats suitable for LLMs and RAGs.
- Make large datasets available across various distribution platforms, including SFTP, data marketplaces, and Databricks Delta Sharing.
- Create and manage bulk data feeds in multiple formats such as delimited text, JSON, Parquet, Avro, etc.
- Manage a team of contractors and build a team of full-time engineers as the project progresses towards completion.
- Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
- Ensure data pipelines are efficient, scalable, and maintainable.
- 5-10 years of experience in data engineering or related fields.
- Proficiency in Python and SQL.
- Experience with various data formats including delimited text, JSON, Parquet, Avro, etc.
- Familiarity with data distribution platforms such as SFTP, data marketplaces, and Databricks.
- Strong understanding of data extraction, transformation, and loading (ETL) processes.
- Proven experience in managing a team of contractors and building a team of full-time engineers.
- Understanding of how to prepare data for machine learning models and retrieval-augmented generation systems.
- Experience with big data technologies and tools.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
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