Data Infrastructure Architect
6 days ago
About Cleo
Cleo is a rapidly growing category leader in ecosystem integration software. We have experienced tremendous growth over recent years and are committed to providing innovative solutions for our customers.
Job Title: Senior Data Engineer
This role offers an exciting opportunity to work with a talented team of professionals who share your passion for data engineering. As a Senior Data Engineer at Cleo, you will be responsible for leading the design, development, and maintenance of data pipelines and infrastructure. Your expertise will play a crucial role in shaping our data architecture and driving business outcomes.
- Your Key Responsibilities:
- Design and develop 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.
- Develop and maintain 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.
- 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 and manage 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.
- 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, including data scientists, ML engineers, product managers, and business leaders, to understand data requirements and ensure data systems meet the needs of AI/ML initiatives.
- 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.
- 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.
- Define and implement data governance practices to ensure the secure, compliant, and ethical use of data in AI/ML workflows.
- Stay current 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.
$150,000 - $200,000 per year
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