The Company

NorthMark Compute & Cloud (NMC²) is backed by dedicated leadership and investment, with a clear mission as it operates at the bleeding edge of technology. Its goal is to scale and enhance the high-performance computing (HPC) and cloud infrastructure that supports its clients' research, production, and delivery, enabling breakthroughs that shape the industries of tomorrow. Its engineers build critical infrastructure to eliminate friction in scientific research, simulations, analysis, and decision-making, accelerating discovery and driving faster innovation.

The Position

We are seeking an experienced Data Architect to own the data strategy, architecture, and governance of the data platform underpinning our next-generation data center, which serves as the foundation for NMC²’s broader compute, cloud, and digital application offerings. This role is key in structuring, securing, and optimizing data flows from OT/IT systems, facility and environmental sensors, compute platforms, cloud services, developer and application platforms, and business applications into a unified, analytics- and AI-ready data environment. Working closely with digital and platform architecture partners, this role ensures a cohesive digital and data ecosystem across physical infrastructure and higher-level platform capabilities.

A core focus of this role is the development of enterprise semantic models, data ontologies, and data anthologies that establish the authoritative layer of meaning across NMC²’s environments. These semantic foundations power digital twins, operational and platform intelligence, reusable data products, and simulation-driven workflows. The role defines how data flows, transforms, and connects across data center infrastructure, HPC clusters, GPU workloads, job schedulers, observability systems, performance and environmental telemetry, and application-facing platforms, enabling consistent measurement, optimization, and planning across the full compute stack.

Responsibilities:

• Define the enterprise data strategy and target-state architecture for data center, and operational domains, ensuring alignment with analytics, AI, digital twin, and simulation use cases.

• Define and govern the enterprise data ontology and data anthology, establishing the authoritative semantic layer that unifies meaning, relationships, and context across data center, and business domains.

• Lead the development of semantic models and ontologies (e.g., Palantir Foundry Ontology) to ensure consistent representation of data workflows, assets, operational processes, and digital systems.

• Establish data domain boundaries and ownership models aligned with data mesh principles, enabling decentralized data ownership with shared semantics and federated governance.

• Design ontology-enabled data abstractions that support digital twins, simulations, scenario modeling, and predictive analytics across compute and infrastructure environments.

• Define canonical data models, semantic contracts, and interoperability standards that unify heterogeneous OT, IT, HPC, and business data sources.

• Architect scalable data models and data flows supporting HPC clusters, GPU workloads, job schedulers, observability systems, and data center infrastructure.

• Design and maintain unified data architectures that connect compute telemetry, environmental data, power and cooling systems, network performance metrics, and enterprise datasets.

• Establish governance frameworks for data quality, lineage, metadata, access controls, lifecycle management, and compliance, balancing control with domain autonomy.

• Enable a data mesh operating model by defining standards for discoverability, data products, semantic consistency, and federated governance.

• Partner with Big Data, data center architecture, and product teams to translate platform and product requirements into scalable, governed data and semantic architectures.

• Define standards for data ingestion, transformation, API integration, and schema management to ensure interoperability across distributed systems.

• Implement and guide adoption of data cataloging, metadata management, and semantic discovery tools to enable self-service analytics and operational transparency.

• Evaluate and integrate emerging technologies in data management, knowledge graphs, observability, simulation tooling, and HPC analytics.

Requirements:

• 7+ years of experience defining enterprise data architecture and data strategy, including ownership of semantic models, governance frameworks, and cross-domain data integration in complex, large-scale environments.

• Strong expertise in semantic modeling, data ontology design, and data anthology development, with the ability to translate complex technical and operational domains into governed semantic layers.

• Demonstrated experience designing or governing enterprise data anthologies, including ontologies, canonical entities, semantic relationships, metadata, and business meaning across domains.

• Experience building or governing enterprise ontologies or semantic data layers using platforms such as Palantir Foundry Ontology, enterprise knowledge graphs.

• Experience designing data mesh–aligned architectures, including domain-oriented data products, federated governance, and shared semantic standards.

• Deep understanding of conceptual, logical, and physical data modeling, taxonomy design, reference data management, and canonical data models.

• Ability to architect digital twin and simulation data models connecting physical infrastructure, compute systems, operational workflows, and predictive modeling outputs.

• Solid understanding of HPC and distributed compute environments, including GPU workloads, job schedulers (e.g., SLURM, Kubernetes), telemetry, and operational metadata.

• Familiarity with cloud-scale analytics and data platforms (e.g., Snowflake, Databricks, BigQuery) and cloud ecosystems (AWS, GCP, Azure) from an architectural and evaluation perspective rather than a data engineering role.

• Experience defining interoperability standards, semantic APIs, schema governance, and cross-system integration patterns.

• Strong grounding in data governance, including metadata, lineage, access control (RBAC/ABAC), lifecycle management, and compliance.

• Systems-thinking mindset with the ability to reason across physical data center environments, HPC compute ecosystems, cloud platforms, and analytical layers.

• Excellent communication and cross-functional leadership skills, able to align engineering, operations, platform, and product teams around a shared data and semantic strategy.