Lead Data & AI Engineer

Overview In this role, you design, build, and optimize scalable data platforms and AI-driven solutions to support enterprise-wide decision-making. You will help Sabert advance its digital transformation by integrating data across manufacturing, supply chain, finance, and other functions. You’ll deliver actionable insights, predictive capabilities, and automation to improve forecasting and operations in a fast-paced manufacturing environment. You will work closely with IT and OT teams to align data solutions with enterprise priorities and governance. This is a hands-on, cross-functional role with meaningful impact on business performance. Compensation / Benefitsfull health insurance (medical, dental, vision)401(k)life insurancedisability insurance ResponsibilitiesDesign and maintain scalable data pipelines from SAP S/4HANA, MES, SCADA, CRM, and other sourcesBuild and manage modern data environments (lakehouse, Azure-based) and ETL/ELT workflowsEnsure data quality, governance, and master data management practicesEnable real-time data ingestion from OT/industrial systems and IoT devicesDevelop, validate, and deploy analytics and ML models (forecasting, predictive maintenance, optimization)Operationalize end-to-end ML pipelines for anomaly detection and process improvementCollaborate with cross-functional partners to translate business problems into analytical solutionsPerform exploratory data analysis to identify patterns and opportunitiesDesign and deploy AI-powered solutions (conversational agents, copilots, automation)Leverage LLMs and agent architectures to accelerate innovation across functionsEstablish reusable AI patterns, documentation, and governance for responsible AIMonitor and improve analytics/AI solutions using metrics and feedbackServe as liaison between IT and OT to align data with operations and enterprise prioritiesDefine and enforce data security, governance, and compliance standardsDocument data architectures, pipelines, models, and solutions for scalability and maintainability Key requirementsStrong expertise in data engineering, modeling, and modern architectures (lakehouse, warehousing, ETL/ELT)Proficiency in Python and SQL for data analysis and ML developmentExperience with Azure, Microsoft Fabric, Databricks, Snowflake, and SAP ecosystemsExperience with Power BI and semantic data modelingHands-on ML skills (regression, classification, clustering, time-series forecasting)Ability to deploy predictive models into productionFamiliarity with AI/ML frameworks, LLMs, and modern AI app developmentUnderstanding of MLOps (model lifecycle, deployment, monitoring, versioning)Experience with industrial IoT, SCADA, MES and real-time data processingKnowledge of manufacturing/Supply Chain/CPG data environments and OT/IT integrationStrong analytical, problem-solving, and stakeholder management skillsExcellent communication and ability to handle multiple priorities in a dynamic environmentstrong communicationstakeholder engagementproblem-solvingdata engineeringdata modelingcloud data platforms