Target roles
CDO, CDAO, Chief Data and AI Officer, EVP or SVP Data and AI, and senior Data, AI and Transformation leadership with genuine authority.
Screened on remit and economics.Mauro Flores
Chief Data & AI Officer / CDAO. Enterprise transformation and business value through data, AI and engineering.
"You cannot AI your way out of data you don't trust."
I help enterprises turn trusted data and AI into value the business and its customers can feel, and I lead the change in how a company decides so it lasts. An executive who has done the engineering, not just sponsored it.
Most recently EVP, Data & AI (Chief Data Officer level) at Virgin Media O2. Open to select CDO, CDAO, Chief AI Officer and EVP Data and AI conversations, in the UK and internationally.
EVP, VMO2 (£10bn) · 300+ data org · 11,000+ enabled · £23M attributed, circa £500M enabled · British Data Awards 2026 winner
Board-level proof
The Customer Trust Indicator, a CEO priority, runs on the data foundations my team built.
The Consumer team owns the survey and the concept. I co-led, and my team built, the Experience Trust Indicator measurement on the enterprise data foundations we created, turning hundreds of real-world signals into a trusted score. It is now one of the company's most important customer measures.
“One of the most impactful leaders I have ever worked with. His vision and leadership helped democratise data at scale, and he combines strategic thinking with genuine care for his team.”
Terry Glenning · Leader of Change & Transformation, Virgin Media O2
Searches where I fit best
Chief Data & AI leadership
CDO, CDAO, Chief Data and AI Officer. Enterprise-wide remit over data, AI, governance,
platform and commercial value.
EVP / SVP Data, AI & Transformation
Senior functional leadership where the role needs engineering depth, operating-model
change and real adoption, not just reporting.
Data & AI value creation
PE-backed, scale-up and enterprise roles where the brief is to mature data and AI and
convert it into measurable commercial outcomes.
Open to Chief Data Officer, Chief Data & AI Officer and Chief AI Officer roles, or similar. I screen on scope, authority and total compensation.
01 / Search Snapshot
Everything you need to assess me for a longlist, in one view.
Built so a researcher or partner can lift the essentials straight into a brief. The detail behind every line is in the CV and the public evidence below.
Sectors and environments
Telco, technology and SaaS, financial services, PE-backed mid-market and large enterprise. Strongest where data and AI are central to value, not a back office.
Comfortable in regulated and complex estates.Geography and work model
UK-based, with a global operating background across the UK, Europe, the US, LATAM and APAC. Permanent roles, hybrid or remote, and open to the right relocation case.
Global operator; British and Mexican national, no UK sponsorship needed.Scope I lead well
Enterprise transformation and operating-model change that turns data and AI into business and customer value: trusted foundations, AI adoption, governance and data products, with commercial accountability. The operating model behind it ran a Centre for Enablement and a Centre of Excellence that certified data and AI assets before they reached production, steered by an enterprise Data Trust Council.
Engineering foundation through to ExCo influence.Availability
I joined Virgin Media O2 with a three-year mission. The foundations are in production, and I stepped down at the natural point to find the next platform to build. Open to the right permanent leadership role, responsive to credible executive searches, and every conversation is handled discreetly.
Discreet, structured executive search.Compensation
Best suited to senior executive remits (EVP, CDO, CDAO) in enterprise-scale or PE-backed businesses, where base, bonus and long-term incentive reflect enterprise ownership. Total compensation is the gate, not title.
Remit and economics screened together, in confidence.02 / Track Record
Proof the work moves beyond strategy slides.
The consistent pattern: turning fragmented systems, unclear ownership and low-trust data into trusted capability the business uses, with the commercial outcomes to show for it.
Agentic automation and AI adoption
Problem: teams needed to move from insight requests to delivery faster, without losing trust, quality or control.
Intervention: I led governed adoption of data agents that connect metadata, trusted business layers and workflow tools, so teams can go from a request to a drafted requirement, user stories and a working prototype, with privacy and quality controls built in. I set the framework and governance; my engineering teams built it.
Result: faster, governed delivery on trusted foundations, with the operating model and controls to scale AI adoption safely.
VMO2 Data Democratisation
Problem: fragmented reporting, weak ownership, manual workarounds and inconsistent definitions.
Intervention: operating model, governed business layers, 250+ certified data products, self-service, support and culture, including Tableau to the Techs for 2,000+ field technicians and Martian Frontier as a data-culture mechanism.
Result: contributed to circa £500M in business value across data-led initiatives, for example more than £250M of fraud helped prevent and £100M+ in network investment optimisation, with at least £23M directly attributed since July 2024 on a conservative basis. Launched 300+ Tableau dashboards and enabled 11,000+ colleagues to self-serve trusted data.
Gemini Enterprise, conversational analytics and data agents
Problem: business teams needed a trusted, governed way to start using AI with data.
Intervention: I set the framework and product roadmap and managed the Google partnership, governance, privacy and adoption while my engineering team built. I governed the running cost of AI as deliberately as the rollout itself, with a FinOps team inside the function working alongside my go-to-market and engineering teams.
Result: all 3,000 available Gemini Enterprise licences deployed on governed foundations, with adoption running ahead of supply, plus conversational analytics and data agents on governed data.
Intelligence from nothing, MuleSoft and Salesforce
Problem: a pre-IPO scale-up with no data strategy behind anything it did for customers, and a global bank's CIO asking for the return on his training, certification, customer-success and support spend.
Intervention: I built the first stack and the first AI models myself and answered the CIO fast on his own data; other account teams asked for the same, and taking it to their customers won CEO sponsorship for a Global Data Strategy & Intelligence team. We proved with AI models that certified partners deliver better results whatever their size, then Salesforce bought us and I ran the playbook when the company was circa 30,000 people: business layers owned by the business, a Centre of Excellence and a Centre for Enablement, and customer success measured by the customer's outcome, a principle their president of customer success set and my team helped build.
Result: a data-driven partner programme in place of a political debate, the EMEA training and certification P&L roughly tenfold through the IPO, and the operating pattern I later delivered at VMO2.
If you are scoping a senior Data and AI leadership hire, I would welcome a confidential note.
Email Mauro confidentially03 / How I Run Data & AI
Foundations, Adoption, Value, Agents.
This is the order I run, and the order is the whole argument. Most organisations start at step four, and that is why their pilots never reach the business.
Foundations
Certified business layers with one named owner per domain, and governance that covers ownership, quality, metadata and lifecycle.
Nine platforms into one governed stack
Adoption
Self-service on data people actually trust, with the training, support and communities that make it stick inside the business.
11,000+ colleagues self-serving
Value
Run as a value centre, not a cost centre. Nothing starts without a case the business has signed off, and realised value is tracked afterwards.
£23M+ directly attributed since July 2024
Agents
AI built only on certified foundations, governed, human in the loop, and owned by a named person in the business.
New agents into production in hours rather than months, once the certified layer was in place
Run it out of order and you get pilots. Run it in order and the business owns the outcome, which is the only version that survives a change of leadership.
£250M+
fraud helped prevent, per the public Tableau customer story
£13M+
annual platform cost removed through consolidation
8,000+
colleagues trained through the Data & AI University
3,000
Gemini Enterprise licences deployed, adoption ahead of supply
How I lead the change
- I open with one question: what do you actually own? Not KPIs, not reporting. How do you measure your success, and what makes you successful? It finds the real owners, and it finds the people who will champion the work.
- I make the partnership a contract. A signed agreement per business domain, with a named sponsor, the domain defined and commitments on both sides. Ownership stops being a conversation and becomes a document.
- Nothing enters the queue without value signed off by the business. Demand management goes live inside three months, and it takes two signatures rather than one.
- I rebuild the roles around the outcomes needed, rather than leaving everyone under the same generic title, and I move the targets from products delivered to value delivered.
- I change behaviour by recognition, not shame. I announce the new way of working before it takes effect, so the argument happens early and in the open, then I recognise the people working the new way and personally help the ones who are stuck.
How I govern AI
- Every agent has a named owner in the business and a data co-owner, inherited from the certified layer it stands on. No owner, no agent.
- An agent never exceeds its owner's permissions. Those limits are enforced in configuration, not in the prompt, so the model cannot talk its way past them.
- Experts evaluate it before launch and keep feeding it afterwards. Where confidence is low the decision goes back to a human, a rule that came out of a real incident rather than a policy document.
- The gate is real. Agents have been refused production when the data underneath them was not certified, including one the business pushed hard for.
"I did not follow a transformation playbook. I wrote one, and it is on its second version."
I authored the self-service analytics, service agreement and data product playbooks that governed how my function worked, peer-reviewed by the senior team and rewritten as the model matured. They exist to kill four failures I see everywhere: reactive order-taking, technical and business people working apart, nobody owning adoption, and quality nobody can trust. I do not run change as theory, I run it as application, and the measure of my work is whether your people still own it after I leave.
Download the one-page operating model
One page, built to forward: the model, the proof and the career arc.
04 / Public Evidence
Independently verifiable, useful for your due diligence.
Public signals you can check without taking the CV at face value: customer stories, interviews, speaking profiles and industry recognition from the data and cloud ecosystem.
Main-stage Keynote
See the Anaplan Connect London 2026 event
Anaplan Connect London 2026 customer keynote
I gave the main customer keynote at Anaplan Connect London 2026, where I made the case that "you cannot AI your way out of data you don't trust", on transforming a large enterprise to decide and create value with data and AI. Named Transformational Leader 2026 in the Anaplan Customer Awards.
Enterprise Case Study
Read the Tableau VMO2 case
study
Virgin Media O2 and Tableau customer story
The public Tableau customer story says Virgin Media O2 helped prevent £250m worth of fraud and blocked 92m+ malicious texts.
Executive Feature
Read the Interface
Magazine feature
Interface Magazine feature
Executive feature on data democratisation at Virgin Media O2, framed as a cultural movement rather than a reporting programme.
Google Cloud Next Speaker
See the Google Cloud Next
speaker profile
Google Cloud Next 2026 speaker profile
Public speaker profile connected to enterprise AI, modernisation and human-centred transformation.
Ecosystem Insights
Read the Atlan VMO2 data
context case
Atlan, Gartner and ReGovern coverage
Public ecosystem references around data context, governance and enterprise adoption at Virgin Media O2.
Executive Podcast
Listen to the C-Suite
Podcast episode
C-Suite Podcast
Conversation from Google Cloud Next on agentic AI, modernisation and human-centred transformation.
Industry Awards
See the British Data Awards
result
British Data Awards 2026, Data Team of the Year
Winner, Data Team of the Year (20+ people), plus finalist for Data Transformation of the Year. Judged by independent panel across the UK data and analytics industry.
Flagship Speaker
See the Women in Data Flagship 2026 post
Women in Data Flagship, main-stage speaker
Main-stage speaker two years running at the Women in Data Flagship: 2025 "CTRL + ALT + INCLUDE: Resetting Bias in Data and AI" and 2026 "From Data Curious to Data Confident", separate from the Women in Data 2026 finalist recognition.
Advisory & Policy
DSIT on GOV.UK
UK Government DSIT Roundtable
In June 2026 I represented Virgin Media O2 at a UK Government roundtable convened by the Department for Science, Innovation and Technology (DSIT), alongside Ian Murray MP (Minister for Digital Government and Data) and senior executives from Unilever, Experian, GSK and RELX, advising on enterprise data-value creation, data exchanges and the regulatory safe spaces that compliant data and AI innovation needs.
The 3-minute story
A short version of the career throughline: engineering foundations, enterprise data scale, AI adoption, and the kind of leadership these searches are hiring for.
If the embedded video is blocked on your network, open the video directly. In three minutes it covers the throughline from engineering and SaaS platforms to enterprise data leadership, practical AI adoption and the kind of executive role I am ready for next.
Engineering foundation
Computer Systems Engineer, graduated top of the generation, with early depth in Java,
API architecture and enterprise platforms.
Enterprise transformation
Operating model work across Salesforce, MuleSoft, Peñoles and Virgin Media O2 to enable
governed business adoption.
Data and AI leadership
Trusted data foundations first, then AI adoption and commercial value, with the
authority and stakeholder reach a senior executive role needs.
05 / For Search Partners
Where I am a strong fit.
Written for retained partners and in-house hiring teams. The searches where I add the most value, and an open door for the ones worth a conversation.
Where I am a strong fit
- Permanent CDO, CDAO or Chief Data and AI Officer roles with enterprise-wide remit
- EVP, SVP or VP Data and AI roles with engineering, platform and transformation depth
- PE portfolio companies maturing data and AI ahead of exit or post-acquisition
- Telco, SaaS, financial services and large enterprises where data and AI drive value
- Roles reporting to the CEO, board or ExCo with genuine authority, budget and decision rights
If the remit is senior, value-led and the compensation is plausible, send it in confidence. I will respond quickly and, where useful, point you to a strong name.
Start a confidential conversation.
A short note is enough: the company or situation, what kind of role it might be, and location or work model if you know them. If the brief is still forming, send what you have. I read every message personally and reply within 24 hours.
CV Downloads
Pick the depth that suits your read.
Same facts, same figures, told at four lengths, plus the two-page executive profile and the one-page operating model if you want the method rather than the history. The profile is the one most search partners ask for. All PDFs open in a new tab.
Executive profile
Download the executive profile
Two pages, written for a search partner
What I am looking for, the evidence from Virgin Media O2 in four parts, the full career back to 2001, and how I work. Built to answer the questions that come up on a first call, so you have them before you make one.
Signature one-pager
Download the one-page operating model
How I build data and AI capability
The operating model on a single page: Foundations, Adoption, Value, Agents, the proof at scale, how I govern AI, how I lead the change, and the full 25-year arc. This is the one to forward to a client or a board.
Fast teaser
Download the one-page profile
One-page profile
The essentials on a single page: seat, scale, headline value and the career arc, for a ten-second read.
Standard
Download the two-page CV
Two-page CV
The default executive CV: full career, selected highlights and recognition, sized for a retained-search read.
Recommended
Download the three-page CV
Three-page CV
My recommended read: the full track record with the depth and proof points behind every line.
Full dossier
Download the four-page dossier
Four-page dossier
The complete dossier for deep due diligence: every role, metric and public proof point in one document.
Career arc
25 years, in two halves.
The first half I built the technology: engineer, architect, founder, and I taught engineering at university. The second half has been about leadership, people and culture, which is what actually changes an organisation. I can still hold a room full of engineers because I was one. That is the advantage, not the headline.
2000 · Mexico
Acer
Where the engineering started: hardware and systems, working on the machines themselves.
2001 - 2002 · Mexico
MBSystems
Networks, software, hardware and systems integration for enterprise
clients including Ford.
2002 - 2007 · Mexico
Tahi Systems
Founder and CEO. Built a technology business from zero, owning sales,
delivery and the P&L.
2007 - 2012 · Mexico
Peñoles
Group transformation in a 60,000-person mining major, and the first
enterprise BI capability. Taught engineering at Tec de Monterrey alongside it.
2012 - 2015 · UK
Infomentum
Senior practice lead, headhunted to the UK. Helped move the firm from
Oracle Gold to Platinum partner.
2015 - 2023 · Global
MuleSoft and Salesforce
Built MuleSoft's data and AI function from nothing, pre-IPO, and won CEO sponsorship
for a global data strategy and intelligence team; then ran the same playbook at Salesforce, circa
30,000 people when I joined and circa 80,000 when I left, through the Tableau and Slack
acquisitions. Held the EMEA training P&L, roughly tenfold, through the IPO and the $6.5B
acquisition.
2023 - 2026 · UK
Virgin Media O2
EVP, Data & AI (Chief Data Officer level). A 300+ extended organisation inside a
£10bn regulated business.
Hardware and systems, consultant, university lecturer, founder, then executive. I have been the engineer doing the work, the founder carrying the revenue and the teacher in front of the room, which is why growing people is not a slide in my strategy, it is the strategy.
Show the full role-by-role history
Virgin Media O2Aug 2023 - Jun 2026
- EVP, Data & AI (Chief Data Officer level) Aug 2023 - Jun 2026
Led enterprise data and AI transformation across a £10bn regulated business, running a 300+ extended data organisation. I joined Virgin Media O2 with a three-year mission. The foundations are in production, and I stepped down at the natural point to find the next platform to build. Full detail in the track record above.
SalesforceJul 2018 - Aug 2023
- Sr. Director, Global Data Strategy & Intelligence Feb 2023 - Aug 2023
- Sr. Director, Global Data Strategy & EMEA Training Feb 2021 - Feb 2023
- Director, Global Data Strategy & Training and Certification Jul 2018 - Feb 2021
Joined Salesforce at the acquisition to run Global Data Strategy & Intelligence for senior leadership, reporting into headquarters in San Francisco, when the company was circa 30,000 people; it was circa 80,000 when I left, through acquisitions including Tableau and Slack. Rebuilt the partner intelligence at that scale and moved the legacy Oracle warehouse to Snowflake with Alation as the catalogue, keeping notebooks, dashboards and automations running throughout. After each acquisition the same customer arrived under several names, so before any prediction was possible we had to match and unify them; that customer-matching layer became the base for AI churn and retention models across the portfolio. Business layers owned by the business as the single source of truth, subject-matter experts owning the definitions, a Centre of Excellence certifying what went to production and a Centre for Enablement so people came to us rather than being forced: influence first, centralisation later. Customer success measured by the customer's outcome: the president of customer success set the principle; my team, with other teams, built the measurement on the business layers, comparable customers classified by size, investment and industry, with a human-reviewed view of the top accounts and the AI models' read on each, later delivered end to end at VMO2 as the Customer Trust Indicator. Led the Org62 and learning-platform integrations through the acquisition, and handed the EMEA Training & Certification P&L back to focus on data and AI.
MuleSoftNov 2015 - Jul 2018
- Head of Global Data Strategy & EMEA Training & Certification Jan 2017 - Jul 2018
- EMEA Regional Lead, Training & Certification Nov 2015 - Dec 2016
Joined pre-IPO, reporting to US headquarters, and found no data strategy behind what the company did for its customers, so I built it: the first stack (AWS to Snowflake, the first AI models in Java and Python, governed self-service BI), then a Global Data Strategy & Intelligence team of data engineers and data scientists with CEO sponsorship, won by answering a global-bank CIO's ROI question fast, on his own data, and by taking it to the other accounts that asked for the same. Hundreds of changes followed, across executive decisions, measurement, automation, dashboards, data products and AI. One example: training success measured by training days instead of enrolments, mapped to platform success and support tickets. Another: proved with AI classification and propensity models that certified partners deliver better results whatever their size, which moved the professional-services-versus-partners debate from opinion to data and funded the partner enablement programme. Held the EMEA Training & Certification P&L, roughly tenfold over the period, and a seat on the EMEA executive leadership team through the scale-up, the IPO and the Salesforce acquisition.
Infomentum2012 - 2015
- Senior Practice Lead, Oracle Middleware & Digital Transformation 2012 - 2015
Headhunted to the UK to set up the Oracle middleware practice and take the firm from Gold to Platinum partner: built the team and an internal certification programme, re-architected McDonald's solution to deliver at scale, and led an intelligence-and-automation build for TRW that modelled vehicle failures by make, age and mileage to predict parts and stock per workshop. Clients included BAE Systems, ICON and PPL; represented Oracle at customers and secured Oracle beta access.
Industrias Peñoles2007 - 2012
- Head of Digital Transformation 2007 - 2012
Reporting to the CIO of a 60,000-employee mining group (UK-listed Fresnillo plc within it): selected Oracle as the backbone after going through the major vendors, brought ADF, WebLogic, WebCenter, SOA and BIEE in against an in-house IT team that had built everything, and won them over with one use case, trained champions and reused libraries. Dynamic, drillable executive scorecards opened the weekly executive meetings; an AI recommendation engine inside the portals surfaced the next document from reading behaviour (2008); the investor-relations portal under the CFO became the company's example of how good looks.
Tecnológico de Monterrey2007 - 2008
- University Professor, Computer Systems & Software Engineering 2007 - 2008
Taught computer systems and software engineering, an early expression of the education and enablement thread that runs through the whole career.
Tahi Systems2002 - 2007
- Founder & CEO 2002 - 2007
Founded and ran a technology business with full P&L ownership for five years: from support and infrastructure into middleware, a first ERP at scale for a construction group, packaged software for doctors, schools and petrol stations, and city portals monetised through advertising.
MBSystems2001 - 2002
- Senior Consultant, Networks, Software & Systems Integration Aug 2001 - Aug 2002
From cabling and PCs to entire enterprise networks for clients including Ford, and the first software and security work.
Platform experience2001 - 2026
Directed, VMO2 2023 to 2026 (I architected and governed; my engineers built): Google Cloud and BigQuery, Atlan, dbt, Tableau, Gemini Enterprise, agents on Google ADK and MCP servers, Anaplan.
Built myself, then with the team I hired, MuleSoft and Salesforce 2015 to 2023: AWS and Aurora, Snowflake, Alation, Yellowfin, AI models in Java and Python, MuleSoft integrations (MuleSoft Certified Architect), Salesforce and Org62, a Slack app on the business layers.
Hands-on, 2001 to 2015: Oracle SOA, WebLogic, WebCenter and WebCenter Sites, ADF, BIEE, IDM, GoldenGate; Java, C#, SQL Server and web services; enterprise networks (CCNA).
Acer2000 - 2001
- Hardware and Systems 2000 - 2001
Where the engineering career began. Building, configuring and repairing machines, then moving into networks and software. I qualified as a Computer Systems Engineer at Tec de Monterrey, studying while I worked.
At this level, success is not measured by the platforms we build. It is measured by the clarity we bring to decisions, the trust we earn with customers, and the confidence with which the business moves forward.