Product Manager (Semantics Adoption & Knowledge Modelling)

Company Description

We're Nagarro. We are a digital product engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale - across all devices and digital mediums, and our people exist everywhere in the world (18 000+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in! By this point in your career, it is not just about the tech you know or how well you can code. It is about what more you want to do with that knowledge. Can you help your teammates proceed in the right direction? Can you tackle the challenges our clients face while always looking to take our solutions one step further to succeed at an even higher level? Yes? You may be ready to join us.

Job Description

Role Purpose

Enable the successful adoption of the platform's semantic abstraction layer by building practical knowledge across EcoStruxure Platform team and the LoBs. This role requires strong knowledge around modelling expertise and bridges the gap between semantic technology and the operational reality of teams that are new to ontology-based approaches.

Core Responsibilities

EcoStruxure Platform team enablement

• Train platform architects and engineers on semantic modeling fundamentals: OWL/RDF/S standards, graph database operation, ontology design patterns, and common pitfalls
• Guide the engineering team on the correct implementation of the ontology management service ensuring technical decisions serve business intent
• Establish EcoStruxure Platform's semantic standards: naming conventions, annotation requirements, foundational ontology alignment patterns, and shared vocabularies (shared responsibility with other teams in Schneider)

• Work directly with each LoB's Subject Matter Experts and Data Modelers to author their first canonical domain models (hands-on, not theoretical)
• Conduct structured working sessions where Subject Matter Experts articulate their domain knowledge and a Data Modeler translates it into formal model design decisions
• Build LoBs confidence and autonomy progressively: start by doing it with them, then coach them, then step back
• Develop reusable guidance materials: modeling guides, worked examples from real business domains, and decision frameworks for common modeling questions (e.g., "when to create a subclass vs. a property," "how granular should the model be")

• Articulate the concrete business value of the semantic layer to business stakeholders in their language, not in technology terms but in terms of: findability, interoperability, intellectual property protection, and reduced integration cost
• Demonstrate tangible outcomes: show how a well-authored canonical model enables faster application development, clearer data product descriptions, and cross-LoB data understanding
• Address skepticism directly and honestly, acknowledge where semantic approaches add overhead and explain where the investment pays back.

• Act as the expert reviewer in the publication gate during the initial platform increments, reviewing submitted models for structural quality, pattern adherence, and interoperability readiness
• Define "what good looks like" for canonical domain models in the platform, with concrete examples Identify and flag modeling anti-patterns early before they become embedded across LoBs

• Hands-on ontology development in industrial or enterprise settings (not purely academic)
• Fluent in OWL, RDF/S, SKOS, SPARQL - as a practitioner, not just a reader
• Experience operating open-standards graph databases (configuration, data loading, querying, performance considerations)
• Demonstrated ability to translate complex domain knowledge from subject-matter experts into formal semantic models
• Experience introducing semantic technologies to teams that have not used them before - with evidence of successful adoption, not just delivery

• Can explain semantic modeling concepts to a non-technical audience without jargon
• Can sit with a control engineer, a grid specialist, or a buildings manager and extract a coherent domain model from their knowledge
• Comfortable working in an environment where the technology is new to everyone - patience, pragmatism, and clarity under ambiguity
• Strong opinions on modeling quality, loosely held when confronted with practical constraints
• Written communication: can produce clear, usable guidance documents - not academic papers
• Oral communication: straight-forward, avoids technical language, is comfortable in multi-cultural environments