Senior Data Engineering Lead

24 hours ago

Raleigh, North Carolina, United States Cleo Full time

Cleo Overview

Cleo is a cloud integration technology company that enables businesses to realize their potential by delivering solutions for connecting and integrating enterprise applications. Our goal is to provide a complete and flexible integration platform that supports critical workflows, enabling our clients to build trusted relationships across their partner ecosystems.

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Job Description: Senior Data Engineer

The Senior Data Engineer will lead the design, development, and maintenance of data pipelines and infrastructure at Cleo. This role involves setting the strategy for data systems, collaborating closely with cross-functional teams, and ensuring the scalability, reliability, and efficiency of data solutions. The Senior Data Engineer will focus on data infrastructure needs for AI/ML models, overseeing the creation of a data warehouse and associated systems from scratch, and ensuring data is properly transformed and optimized for machine learning and artificial intelligence applications.

  • Key Responsibilities:
  • Lead the design and build of data pipelines: Develop and maintain scalable, reliable, and efficient data pipelines that collect, process, and store large datasets. Ensure these systems are optimized for AI/ML model training and inference.
  • Set data infrastructure strategy: Define and execute the strategy for building and maintaining data warehouses, data lakes, and other data storage systems that support both operational and analytical needs. Ensure systems are optimized for AI/ML model workflows.
  • Hands-on data transformation for AI/ML models: Design and implement data transformation processes, including feature engineering, data preprocessing, and data augmentation, to ensure data is in the right format for machine learning models.
  • Build data structures and metadata management: Build and manage the data structures, metadata repositories, and related systems that support data transformation, ensuring the organization has well-documented, accurate, and accessible data for AI/ML workflows.
  • Data quality controls and risk management: Establish and implement data quality controls to ensure that data used in machine learning models is clean, consistent, and accurate. Identify and raise risks at all stages of the data engineering process, including data ingestion, transformation, and storage.
  • Collaborate with cross-functional teams: Work closely with data scientists, ML engineers, product managers, and business leaders to understand data requirements and ensure data systems meet the needs of AI/ML initiatives. Provide hands-on leadership to guide teams in transforming data for model training.
  • ETL development and optimization for AI/ML: Lead the development and optimization of ETL (Extract, Transform, Load) processes for ML/AI data. Focus on efficiently moving and transforming large datasets, ensuring data quality and readiness for model training and deployment.
  • Optimize data for model training and inference: Ensure data pipelines are designed to support the needs of AI/ML model training, such as handling large volumes of data, managing data quality, and enabling fast model iteration.
  • Data governance for AI/ML: Define and implement data governance practices to ensure the secure, compliant, and ethical use of data in AI/ML workflows.
  • Stay current: Keep up with emerging trends in data engineering, particularly those related to AI/ML model development, and implement best practices to optimize data systems for machine learning.

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