Top Data Engineering Jobs in Lexington, KY

The Machine Learning Architect will design and implement ML solutions on cloud platforms, working directly with customers to align strategies with business goals. Responsibilities include end-to-end project management, model deployment, and maintaining strong customer relationships.

The Front Office Technology Architect will design and maintain Salesforce solutions, lead technical teams, and implement AI technologies, ensuring performance, security, and compliance.

The Senior Technical Program Manager will drive strategic initiatives within the Customer Platform Engineering team to enhance engineering and platform capabilities, ensuring timely delivery of complex technical programs while optimizing processes and fostering operational excellence through continuous improvement.

The role involves designing and maintaining ETL pipelines, transforming data, and building scalable datasets for reporting and analytics in healthcare.

The Senior Data Engineer will analyze complex data sets, curate data sources, design reports, and collaborate to ensure data quality and insights delivery.

Lead the fraud strategy to protect customers and card portfolios, develop real-time models with Data Science, and manage fraud performance metrics. Collaborate across teams to implement effective fraud controls and optimize processes.

Manage end-to-end data migration projects, ensuring accurate data mapping, quality assurance, collaboration across teams, and effective client communication.

Lead the design and implementation of scalable data architectures while managing a team of data engineers and driving cross-functional analytics to support supply and R&D functions.

The Actuary Prophet Developer will lead Prophet model development, collaborate with IT and stakeholders, optimize model performance, train users, and guide junior staff in actuarial methodologies. Responsibilities include model architecture, testing, and compliance with model risk management standards.

Design and optimize data pipelines, work with structured/unstructured data, ensure data quality for analytics, and collaborate with data scientists.