Senior Lead Software Product Data Engineer, GSS Software Engineering

Allegion • Carmel, IN

3d

About The Position

This position serves as the senior architectural authority for product data across Allegion's Global Software Solutions Business units, owning the translation of the product software application data standards into a single, unified opening/device/customer data model embedded in the products. The Senior Lead Software Product Data Engineer is the go-to authority for GSS data architecture, pipelines, and platform standards, while actively partnering with each business unit's engineering team to bring their systems onto the shared model. This role collaborates closely with the Enterprise Data Architects, AI and the Data & Analytics Team, and with Engineering leads across GSS's product lines, balancing deep architectural ownership with cross-functional partnership and enablement. Qualified candidates must be legally authorized to be employed in the United States. The company does not intend to provide sponsorship for employment visa status (e.g., H-1B, TN, etc.) for this employment position.

Requirements

• 8+ years of experience in data architecture, data modeling or data engineering, including at least 3-5 years designing a shared or central data platform that multiple product engineering teams built on top of — not just a data warehouse teams queried.
• Proven experience translating conceptual or logical data models into physical data architecture, pipelines, and APIs in partnership with product engineering teams.
• Experience building and maintaining an enterprise data catalog or comparable metadata/governance repository.
• Direct experience unifying data models and pipelines across independently run business units or product lines at enterprise scale, ideally inside a company that grew through M&A.
• Experience designing or running technical data-onboarding processes for bringing new business units, systems, or data domains onto a shared platform.
• Hands-on fluency with modern data-platform tooling: a lakehouse or warehouse (Databricks, Snowflake, or BigQuery), object storage (S3/ADLS/GCS), orchestration (Airflow or dbt), and event streaming (Kafka) for device/telemetry ingestion; strong SQL and Python.
• Comfort with device/IoT telemetry at scale — ingesting, aggregating, and modeling data from hundreds of thousands to millions of connected devices, structured in layered zones (bronze/silver/gold or equivalent) so raw device events and governed, query-ready models coexist.
• A track record of defining data standards — schema, lineage, quality, governance — that actually got adopted, without having direct authority over the teams being asked to change.
• Understanding of metadata management and data cataloging practices, and how well-curated, governed data improves AI/LLM consumption of product and device data.
• Demonstrated ability to identify and communicate technical/architectural risk to senior leadership in terms of business impact, not just technical detail.
• Excellent communication skills — able to translate between product engineering teams across business units and senior leadership.
• Strong facilitation skills for architecture reviews spanning multiple product teams with different priorities.
• Familiarity with master data management, data governance, and enterprise architecture frameworks (e.g., DAMA-DMBOK).
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience; Master's preferred.

Responsibilities

• Document how each software application across business units generates, defines, and uses data, building an application-by-application and business unit view of data reality across the organization.
• Roll business-unit SW Application & Platform knowledge up into a single GSS -level standard, and produce a clear, business-unit-by-business-unit comparison of where practices align with that standard and where they diverge — surfacing exactly where the highest-impact improvements can be made, so the organization can prioritize with evidence instead of guesswork.
• Translate application conceptual & logical data model — into physical schemas, Data Interfaces, pipelines, semantic layers and downstream systems such as APIs embedded in GSS's software products, starting with the DB convergence.
• Own and maintain a comprehensive GSS SW product data catalog, the authoritative record of data types, data definitions, data standards, data storage and data accountability — so every team works from the same source of truth instead of tribal knowledge.
• Design the data lake and digital-twin architecture for GSS's connected device install base —so device data becomes governed, queryable inventory to drive actional insights instead of scattered per-product logs.
• Implement a standard, repeatable technical process for bringing each business unit's product data — Overtur, Partner Solutions, Global Readers & Credentials, Waitwhile, and GSS Access Solutions — onto the shared schema and platform, so each is modeled and integrated consistently from day one.
• Put data-model, lineage, and quality standards onto the organization's approved-to-enforced CI gate. Maintain and continuously refine the GSS SW knowledge base — the entity definitions, business rules, models, and catalog built through the work above — as the single, trusted source every data team across the organization builds from.
• Partner with the identity/CIAM workstream so any shared AI/agent platform is built on a unified data model and correctly scoped access, not fragmented, over-privileged data pulled from five separate systems.
• Serve as the named technical reviewer for the data layer of the EU Cyber Resilience Act (December 2027) compliance program, ensuring OSDP migration and security-by-design requirements are met in the physical data architecture.
• Audit each business unit's product-level data implementation against the shared platform standard, surfacing misalignment early and feeding findings back before they compound into rework.
• Partner with each business unit's engineering team, applying the shared schema and platform so they build in a standard-aligned way instead of independently re-solving the same data problem a third or fourth time.
• Partner with the Enterprise Data Architect and Allegion's AI Organization to ensure the product-side data platform and device telemetry are exposed in a form agentic AI systems can consume reliably, extending centralized context from the conceptual layer into the physical one.
• Track how agentic AI and other AI-driven tools consume data and evolve the knowledge base’s modeling and cataloging standards, so it stays usable by both people and AI systems.
• Partner with Allegion’s AI Organization to share this centralized data context, enabling additional AI use cases beyond the data organization’s own delivery work.
• Act as the Data Organization’s internal advocate for treating context curation as shared strategic infrastructure and a competitive advantage.

Benefits

• Health, dental and vision insurance coverage
• 401K plan, offering a 6% company match and no vesting period
• Tuition Reimbursement
• Unlimited PTO
• Employee Discounts through Perks at Work
• Community involvement and opportunities to give back
• Opportunities to leverage your unique strengths through Clifton Strengths testing and coaching

What This Job Offers

• Job Type: Full-time
• Career Level: Senior