Manager Engineering Management

Vertiv•Westerville, OH

About The Position

Global Analytics Engineering Manager with Agentic AI He or She will lead the bridge between Operational Technology (OT) and Information Technology (IT) for the Global Technology Eco-System at Vertiv. This is a high-impact leadership role responsible for architecting and managing the overall manufacturing data journey. You will deal with our overall transformation and support our connected factory floor to drive standardized, scalable, reliable data models that drive global manufacturing excellence and AI-driven transformation.

Requirements

Experience: 2+ years in Data Engineering, Analytics, or Industrial Automation.
Domain Knowledge: Proven track record in a manufacturing environment (discrete or process) with an understanding of MES and ERP integration.
Systems Thinking: Ability to navigate the complexities of IT/OT convergence and the security requirements of a global manufacturing footprint.

Nice To Haves

Experience with Digital Twin concepts and Discrete Event Simulation.
Familiarity with AI/ML deployment at the edge.
Experience supporting global initiatives within a large-scale manufacturing organization

Responsibilities

Industrial Edge & Connectivity Architecture
Edge Orchestration: Deploy industrial edge computing strategies as well as Large Video Modeling using Linux-based gateways and Docker containerization to run ingestion logic at the plant level.
Ignition Modules: Tag Historian, Transaction Groups, Alarm Notification Pipelines, and Named Queries
Programming: Python, SQL, C#, VBScript, PowerShell, PowerApps, Agent & Bot creation
Databases: MS SQL Server (Stored Procedures, Views, Indexing), MySQL, Azure SQL
Protocols: OPC UA/DA, MQTT Sparkplug B, Modbus TCP/IP, Transcoder, Profinet, EtherNet/IP
DevOps: Azure DevOps (YAML Pipelines, Repos, Boards), GitHub, Bitbucket, CI/CD Automation
Secure Networking: Partner with IT to implement robust TCP/IP, Transcoder, and VLAN segmentation strategies, ensuring secure data extraction across the overall Model without impacting production uptime.
Data Engineering & Modeling
Modern Data Stack Leadership: Drive Standardize structures to create Golden Tables (Snowflake/BigQuery/AWS), ensuring a single source of truth for manufacturing KPIs like OEE and OOE.
AWS Ecosystem Management: Leverage AWS partner solutions (e.g., EasyEdge, CloudRail, Edge2Web) to accelerate sensor-to-Open API onboarding and real-time visualization.
Pipeline Reliability: Implement software engineering best practices, including version control (Git), CI/CD, and rigorous data quality testing for high-frequency industrial data.
Strategic Alignment: Translate complex factory floor challenges into data initiatives that support global initiatives and operational excellence.