Fractional Chief Data Officer (In-Fund, Mid-Market Private Equity)

Fractional Chief Data Officer (In-Fund, Mid-Market Private Equity)

Client: Mid-market Private Equity fund (value creation team)

Engagement: Fractional, long-term relationship, portfolio-facing

Time commitment: Typically 1 day/week baseline, with flexibility to ramp to 2 to 3 days/week in bursts during deals and early post-deal periods

Focus: UK-led portfolio, remote-first with occasional in-person where helpful

The opportunity

A mid-market PE firm is strengthening its portfolio value creation capability in data and AI, treating “getting the foundations right” as an early, high-leverage intervention in new deals and early-hold assets. They want a Fractional CDO who can operate at PE pace, quickly work out what matters in each business, then shape, scope and steer the work that needs doing.

This is not a hands-on build role. The core requirement is project scoping and senior stakeholder alignment, followed by selecting and managing external delivery partners or portfolio teams to execute. You retain technical ownership, ensure quality, and keep spend proportionate.

The fund invests in B2B businesses across software, tech-enabled services, business services, and selected financial services. Many assets are mid-market and not “enterprise mature”, so pragmatism is essential.

Must Haves:

• Must be an ex-Consultant (MBB, Tier 2 or Boutique)
• Must have B2B Experience (SaaS, Tech-Services or other)
• Must have Venture Capital or Private Equity
• Must have experience with architecture reviews, data warehouse optimisation and tech-stack selection
• Must be open to Fractional work.
• Must have experience with AI value creation

What you will do:

You will parachute into pre-deal and early post-deal situations, as well as the broader portfolio, working closely with the deal and value creation teams, plus portfolio CEOs/CFOs/CTOs to set companies up for successful exits.

1) Diagnose data and systems maturity fast

• Run a structured diagnostic using the data room, management Q&A, and targeted technical discovery.
• Review how key functions generate and use information (typically finance, operations, commercial, and people data).
• Identify where the truth breaks down: source system quality, inconsistent definitions, gaps between systems, and reporting limitations.

2) Convert the equity story into a clear data agenda

• Translate the investment thesis and value levers into specific data requirements and KPI definitions.
• Clarify what needs to be measurable for decision-making (commercial performance, delivery economics, operational efficiency, cash and working capital, etc.).
• Establish a pragmatic performance measurement approach that works at mid-market maturity levels.

3) Set direction on architecture and tooling, without over-engineering

• Provide judgement on target-state architecture and the right pattern for the asset: warehouse or lakehouse, semantic layer, BI approach, integration method.
• Support major platform decisions (e.g. Snowflake vs Databricks), and ensure alignment with core business systems, including ERP and CRM evolution.
• Make build vs buy recommendations that fit timeline, budget, and internal capability.

4) Define standards and governance that enable speed

• Put in place minimum viable governance: shared definitions, hierarchies/dimensions, ownership, access controls, and data quality measures.
• Ensure consolidated reporting is possible across entities where there have been acquisitions or group structures.
• Keep governance lightweight, focused on comparability and confidence in numbers.

5) Scope and sequence the delivery plan

• Produce a phased roadmap and mobilisation plan, typically covering first 30–60 days and 100-day priorities.
• Document dependencies, resourcing needs, partner roles, cost ranges, and measurable outcomes.
• Create a clear brief that a delivery partner can execute against, with benefits tracking baked in.

6) Select, challenge and run delivery partners

• Choose the right external partners, set scope, and run governance so delivery stays outcomes-led.
• Apply commercial discipline: challenge timelines and budgets, prevent “consulting bloat”, and hold quality gates.
• Stay close enough to ensure the build is right, without being the person writing pipelines.

7) Improve executive visibility through reporting and dashboards

• Ensure leadership teams have timely, trusted reporting and dashboards that reflect business reality.
• Prioritise investor-grade KPI visibility that supports board conversations and, ultimately, exit readiness.

8) Prepare the ground for automation, AI and new data value

• Define what “data-ready” looks like for automation and AI, and how to get there sensibly.
• Where relevant, help identify and triage analytics/AI opportunities across functions, based on ROI and readiness.
• In some assets, explore data commercialisation or analytics products as a value lever, where credible.

Internal enablement

• Contribute to the fund’s own firm-level view on data and AI (standards, reusable patterns, assessment approach).
• Support investment and portfolio teams with practical guidance on data diagnostics and AI opportunity spotting during diligence and the first 100 days.