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Cenotian
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Europe, flexible, with regular travel to partner, customer, and operator sites

VP, Data

This is a role for an exceptionally fast, technically sharp, and obsessively data-driven generalist to own Cenotian's entire data layer: the compounding ontology, the pipelines, the models, and the data product experience that turn every deal into proprietary, queryable, reusable intelligence inside the world's first asset-backed financing platform purpose-built for robotics and industrial automation.

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We are not hiring a credit analyst, a risk specialist, or an underwriter. We are hiring a data obsessive with builder instincts, technical leverage, and relentless judgment about what is true: someone who ships data machinery, codes tools, normalizes chaos into structure, and builds the compounding data ontology that becomes Cenotian's institutional moat.

Context

Data is not a support function here. Data is the product, the moat, and the reason Cenotian compounds. Every deal must leave behind structured, enriched, reusable facts rather than documents in a folder. Your job is to make that happen by default, at speed, forever. Robotics and industrial automation are entering an explosive adoption super-cycle, comparable to the formative decades of aircraft leasing, telecom towers, and hyperscale data centers. Cenotian exists to win the deployment layer of that super-cycle: the financing, trust, and infrastructure that lets OEMs, system integrators, and operators scale without balance-sheet exposure or stranded risk. Traditional leasing models fail due to their inherent need for residual value, redeployability, and secondary markets. OEMs and system integrators shoulder financing risk, while operators resist capex and stranded risk. Cenotian has solved this structural problem. Cenotian provides asset-backed financing infrastructure that enables OEMs and integrators to scale globally, operators to deploy automation without balance-sheet exposure or stranded risk, and institutional investors to access a new, prime fixed-income asset class without operational involvement in warehousing, spares, maintenance, or asset operations. The lesson of every prior asset super-cycle is clear: the financing layer wins when it sees the market, standardizes the information, and compounds track record faster than competitors. Cenotian's equivalent edge is a proprietary ontology across counterparties, equipment, systems, contracts, milestones, cash flows, technical evidence, telemetry, and outcomes. Cenotian's data infrastructure, including the MIS and knowledge graph, ontological warehouse, ingestion backbone, and model stack, is core to making the entire platform institutional, auditable, and scalable without scaling headcount proportionally.

Position overview

The VP, Data owns Cenotian's data layer end-to-end: the compounding ontology, ingestion pipelines, warehouse, models, data quality, and product experience that turn messy operating reality into structured intelligence the company can query and trust. Data judgment sits at the core. This is a hands-on executive role built around a fast, automated, institutional-grade data machine that is auditable, scalable, and exception-driven. You will set the architecture and standards, but you will also query the data, build pipelines, prototype tools, and resolve difficult modeling decisions personally. This is a field role. You will spend real time with partners, customers, system integrators, operators, and Cenotian's own teams because the richest and most defensible data opportunities are discovered where transactions and deployments actually happen.

Reports to:
Founders / Managing Partners
Location:
Europe, flexible, with regular travel to partner, customer, and operator sites

Key responsibilities

  1. 01

    The compounding data ontology

    • Own the canonical ontology: the authoritative way Cenotian represents counterparties, equipment, systems, applications, contracts, milestones, cash flows, covenants, technical evidence, telemetry, and outcomes.
    • Establish stable identifiers, entity relationships, definitions, ownership, and versioning across the data model.
    • Ensure every interaction enriches the ontology with structured facts rather than isolated documents.
    • Make key deal, contract, technical, and outcome fields consistently populated, joined, queryable, and reusable across the portfolio.
    • Wire the ontology into the MIS and operating workflows so it becomes the company's single source of truth rather than a side database.
  2. 02

    Data monopolization in the field

    • Spend real time with system integrators, OEMs, operators, customers, and internal operating teams.
    • Observe how data originates, where it lives, what is captured today, and what disappears through current workflows.
    • Identify new signals worth capturing across technical qualification, project delivery, payment behavior, servicing, supply chain, and telemetry.
    • Co-design ingestion methods that fit how customers and operators actually work so that producing data is frictionless for them and automatic for Cenotian.
    • Turn every field visit into concrete additions to sources, schemas, fields, rights, or capture mechanisms.
  3. 03

    Ingestion, pipelines and normalization

    • Build and govern ingestion from documents, forms, portals, accounting and banking exports, registries, OEM and SI systems, filings, supply-chain data, telemetry, and internal tools.
    • Automate extraction, entity resolution, deduplication, enrichment, validation, and freshness checks.
    • Define data contracts, lineage, quality controls, permissions, and exception handling that preserve auditability.
    • Make adding a new source fast without allowing local shortcuts to fragment the ontology.
    • Build agent-assisted retrieval so data is discoverable on demand rather than trapped in inboxes, PDFs, or individual memory.
  4. 04

    The model stack

    • Build and maintain the model layer on top of the ontology across eligibility, qualification, contracts, decisions, outcomes, and portfolio monitoring.
    • Encode machine-readable contractual primitives, acceptance criteria, covenants, decision records, and outcome history.
    • Prefer rules-first, deterministic, and explainable logic where decisions require traceability.
    • Introduce statistical models and AI overlays where they improve a defined decision without weakening auditability.
    • Turn misses, exceptions, and postmortems into improved benchmarks, scoring, stress tests, and capture requirements.
  5. 05

    Data product experience

    • Own the data lifecycle as a product surface: intake, structuring, enrichment, monitoring, reporting, and user feedback.
    • Make the system exception-driven so humans intervene on genuine ambiguity rather than re-keying routine information.
    • Give Finance, Commercial, Technical Qualification, Legal, and Engineering self-service access to trustworthy data at the point of decision.
    • Define clear standards for freshness, coverage, latency, confidence, provenance, and permissions.
    • Ensure every internal or external data product improves the underlying data asset through use.
  6. 06

    Data tooling, automation and technical leadership

    • Ship the first version yourself, query data directly, and prototype tools and decision-support systems instead of writing specifications and waiting.
    • Use SQL, code, APIs, workflow tools, and modern AI coding agents as part of daily execution.
    • Set Cenotian's data architecture, technical standards, roadmap, and quality bar in partnership with the Head of Engineering.
    • Decide what should remain a lightweight internal tool and what requires production hardening.
    • Build a small, exceptional data team only as the workload and system maturity justify it.
  7. 07

    Monitoring, deal support and cross-functional execution

    • Build data-driven portfolio monitoring that surfaces exceptions early and automatically, including missed milestones, payment drift, utilization anomalies, covenant breaches, and servicing concerns.
    • Support live deals with the structured facts and pipelines that make decision-grade memos and auditable mechanics possible.
    • Work with Commercial to shape data at origination rather than repair it after handoff.
    • Work with Technical Qualification to turn field evidence into reusable facts and benchmarks.
    • Work with Legal to keep data rights, contractual primitives, and obligations machine-readable.
    • Partner with Special Missions on new data-intensive bets while protecting the coherence of the canonical layer.

You will know it is working when

Data flows from source to ontology without manual re-entry across the company's core workflows.

Key deal, contract, technical, and outcome fields are queryable, consistently populated, and compounding across the portfolio.

Every transaction, call, and document leaves behind structured, reusable facts.

Finance and operating teams can self-serve trustworthy data instead of reconstructing it.

Monitoring surfaces exceptions early and automatically.

Field visits routinely produce new sources and capture mechanisms that land in the graph.

Candidate profile

The Data-Obsessed AI Engineer profile

Personality

Skills and competencies

Organizational context

The VP, Data reports directly to the Founders and will work most closely with:

  • CCO / Head of Commercial - origination and counterparty data, customer workflows, and structured signal early in the funnel.
  • CFO / Head of Finance - decision data, portfolio strategy, capital-markets readiness, monitoring, and reporting.
  • Head of Engineering - ingestion, infrastructure, automation, security, auditability, and the hardening of prototypes.
  • General Counsel - data rights, contractual primitives, governance, and lawful reuse.
  • Special Missions Group - data-intensive missions, rapid product tests, and new sources that must compound into the canonical layer.
  • The Principal, Special Missions: Data Products & AI launches products and workflows on top of the canonical layer. The VP, Data owns the ontology, architecture, standards, and long-term integrity of that layer.

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