Data Engineer

Global Financial Services Client | Hybrid, Chicago, IL

Contract: 12 Months

Work Arrangement: Hybrid, 4 days onsite per week

Position Summary

This individual will design, develop, and maintain data pipelines that support analytics, machine learning, and AI applications. The role involves working with data scientists, engineering teams, and business stakeholders to integrate data from multiple sources, improve data quality, and support the deployment and maintenance of machine learning solutions.

Key Responsibilities

• Design, develop, and maintain data pipelines supporting analytics, machine learning, and AI applications.
• Build and optimize data integrations, transformations, and preprocessing workflows across multiple data sources.
• Develop cloud-based data engineering solutions using Python, SQL, and distributed processing technologies such as PySpark.
• Support MLOps processes, including machine learning model deployment, integration, monitoring, and production support.
• Assist with migrating and modernizing legacy data processes into cloud-based environments, preferably AWS.
• Prepare, clean, and transform datasets for predictive analytics, machine learning models, and AI applications.
• Troubleshoot data pipeline failures, resolve data quality issues, and optimize processing performance.
• Collaborate with data scientists, developers, and business stakeholders to translate business requirements into technical solutions.
• Support integration of machine learning models and emerging AI technologies, including large language models (LLMs).
• Document technical processes, workflows, and data engineering solutions to support ongoing maintenance and development.

If interested in more details, please apply!