Director, Enterprise Data and AI Architecture

We’re looking for people who put their innovation to work to advance our success – and their own. Join an organization that ensures a more secure world through connecting and protecting our customers with inventive electrical solutions.

WHAT YOU WILL EXPERIENCE IN THIS POSITION:

As Director of Enterprise Data and AI Architecture at nVent, you will define the technical direction for how data and AI create value across the organization. You will shape our multi-year architecture strategy, enable enterprise reference architectures, and partner with executives to align technology investment with business priorities, while staying engaged in the technology to make sound, hands-on design decisions. This role demands architectural vision, deep technical expertise across the modern data, analytics, and AI stack, and the business acumen to translate complex data challenges into solutions that deliver lasting value.

You are a change agent with an enterprise architecture focus who will engage leadership stakeholders on our highest priority initiatives and business transformation programs. You will define the blueprint for how governed data flows to power decision-making and AI solutions across the organization, and you will set the architecture standards, principles, and governance that keep that blueprint coherent as our company scales and grows.

Key Responsibilities:

Enterprise Architecture & Strategic Technical Leadership:

• Own the enterprise data and AI reference architecture and the multi-year technology roadmap to deliver it, keeping near-term delivery aligned with long-term strategic direction

• Set the technical strategy and architectural principles for the modern data stack and AI, translating enterprise business strategy into the capabilities required to support it

• Lead architecture governance, defining the standards, patterns, and decision authority that keep solutions coherent across domains, platforms, and acquired entities

• Advise executive leadership on build versus buy, platform strategies, and technology investment decisions, framing the options, tradeoffs, and the nature of the value each path can unlock

• Shape the data and AI operating model in partnership with leadership, defining how architecture, engineering, governance, and data capabilities deliver enterprise outcomes

Solution Architecture & Design:

• Lead end-to-end architecture design for enterprise data solutions spanning ingestion, integration, storage, modeling, and consumption layers, designed to scale from MVP to enterprise value

• Architect master data management (MDM) solutions covering critical data domains with a focus on data quality, matching, survivorship, and golden record management

• Define and maintain architecture decision records and ensure designs adhere to enterprise standards for security, regulatory compliance, and performance, embedding data governance from the start

• Own vendor relationships and technical roadmaps across the data platform ecosystem, partnering to evaluate emerging capabilities and plan platform investments

Project Leadership:

• Lead complex data projects from discovery through production delivery, managing scope, dependencies, risks, and timelines

• Engage in M&A integration projects, designing data integration strategies, harmonization approaches, and migration plans that enable rapid time-to-value from acquisitions

• Manage multiple concurrent initiatives with competing priorities, maintaining quality and architectural integrity under delivery pressure

Data Integration:

• Design and implement data integration patterns across batch, near-real-time, and streaming workloads connecting ERPs, SaaS platforms, operational systems, and the enterprise data platform

• Establish standards for ETL/ELT pipeline design using tools such as dbt, Matillion, Informatica, and Snowflake native capabilities, and define data modeling standards for analytical and operational workloads

AI & Advanced Analytics Architecture:

• Define our architecture for AI and machine learning, spanning data pipelines, feature management, model serving, and the patterns for generative AI such as retrieval-augmented generation, vector stores, and agentic workflows

• Ensure adherence to standards for responsible and governed AI by design, embedding the controls of a recognized framework such as the NIST AI Risk Management Framework into solution architecture from the start

• Architect the analytics and semantic layer that delivers trusted, consistent metrics to business intellige