Lead Data & AI Engineer

Brief Description The Data & AI Platform Engineer at Sabert Corporation plays a strategic and hands-on role at the intersection of data engineering, advanced analytics, and artificial intelligence. This position is responsible for designing, building, and optimizing scalable data platforms and AI-driven solutions that support enterprise-wide decision-making.

This role is instrumental in advancing Sabert’s digital transformation by integrating data across manufacturing, supply chain, finance, sales, HR, and customer service functions. The engineer delivers actionable insights, predictive capabilities, and intelligent automation that enhance operational efficiency, improve forecasting accuracy, and drive business performance across a fast-paced, manufacturing-driven environment.

Essential Duties & Responsibilities

• Design, develop, and maintain scalable, reliable data pipelines integrating structured and unstructured data from systems such as SAP S/4HANA, MES, SCADA, CRM, and other enterprise platforms.
• Build and manage modern enterprise data environments, including Microsoft Fabric, Azure-based lakehouse architectures, and ETL/ELT pipelines.
• Ensure high-quality, governed, and trusted data through implementation of data quality frameworks, validation processes, and consistency checks.
• Establish and maintain master data management (MDM) practices and enforce enterprise data governance standards.
• Enable real-time and near real-time data ingestion and processing from manufacturing systems, industrial IoT devices, and operational technology (OT) environments.
• Develop, validate, and deploy advanced analytics and machine learning models, including demand forecasting, predictive maintenance, supply chain optimization, and financial planning models.
• Build and operationalize end-to-end machine learning pipelines supporting anomaly detection, process optimization, and performance improvement.
• Collaborate with cross-functional business partners to translate complex business challenges into scalable analytical solutions and production-ready AI models.
• Perform exploratory data analysis to identify patterns, trends, and insights that drive continuous improvement across operations.
• Design, develop, and deploy AI-powered solutions such as conversational agents, copilots, and workflow automation tools to enhance productivity.
• Leverage modern AI frameworks, including large language models (LLMs) and agent-based architectures, to accelerate innovation across business functions.
• Establish reusable AI solution patterns, documentation, best practices, and governance guardrails for responsible AI adoption.
• Monitor, evaluate, and continuously improve deployed analytics and AI solutions based on performance metrics and stakeholder feedback.
• Serve as a key liaison between IT and OT teams, ensuring alignment of data solutions with plant operations and enterprise priorities.
• Define and enforce enterprise data security, governance, and compliance standards in alignment with regulatory and company requirements.
• Document data architectures, pipelines, models, and solutions to support knowledge sharing, scalability, and maintainability.

Required Knowledge, Skills, And Abilities

• Strong expertise in data engineering, data modeling, database design, and modern data architectures (lakehouse, data warehousing, ETL/ELT).
• Proficiency in Python and SQL for data analysis, pipeline development, and machine learning model creation.
• Experience with cloud platforms such as Microsoft Azure, Microsoft Fabric, Databricks, or Snowflake, and integration with SAP ecosystems.
• Strong experience with data visualization and business intelligence tools, including Power BI and semantic data modeling.
• Hands-on experience with machine learning techniques, including regression, classification, clustering, and time-series forecasting.
• Proven ability to deploy predictive models and analytics solutions into production environments.
• Familiarity with AI/ML frameworks, large language models (LLMs), and modern AI application development.
• Understanding of MLOps practices, including model lifecycle management, deployment, monitoring, and version control.
• Experience working with industrial IoT, SCADA, and MES systems, including real-time data processing.
• Knowledge of manufacturing, supply chain, or CPG data environments, with an understanding of OT/IT integration challenges.
• Strong analytical thinking, pro…