In numbers
and technology
five disciplines
across four markets
in production
Leadership without leaving the technology behind
I lead the Data function the way I think it should be led: close enough to the architecture to make real technical decisions, and far enough back to design the team, the operating model and the priorities that turn data work into business impact. In practice that means I stay directly involved in architectural decisions, still write dbt models and SQL, review data models and semantic definitions, and mentor the team — alongside hiring, career development and making sure Data improves how the business decides.
Leadership
- Data strategy and operating model — how the function is organized, prioritized and held accountable
- Team leadership across five disciplines: Data Engineering, Analytics Engineering, Analytics, BI and Data Science / Product Science
- Hiring and career development — building progression paths in a multidisciplinary team
- Executive stakeholder alignment — translating business strategy into data priorities, and data reality back into decisions
- Governance — metric ownership, definitions and data quality as shared organizational assets
- Cross-functional partnership with Marketing, Sales, Finance, Product and academic operations
Technical depth
- Analytics Engineering with dbt — modeling, tests, exposures, lineage and software-engineering practice applied to analytics
- Semantic modeling and governed metrics — one set of definitions for humans, BI tools and AI agents
- Modern data platforms — AWS, Iceberg, Athena/Trino, Snowflake and Redshift across different environments; orchestration with Prefect and Airflow
- Data modeling and architecture — SQL and Python, hands-on
- BI architecture and BI-as-Code — applying version control, reusable models and software-engineering practices to analytics delivery
- Agentic analytics — governed, semantically described data access for AI agents over MCP
The Data-to-Decision Chain
Most companies don't have a data problem. They have a decision problem: data everywhere, and still no reliable way to turn it into action — and now AI stacked on top of the same broken foundation. This is the chain I build. Build it whole and in order, and the organization decides well. Miss a link, and everything below it wobbles.
Technology enables it. People and the operating model create it.
Read the essay behind this →Experience
Engineering and Big Data, then technical leadership as CTO, then Analytics Engineering and data platforms, now Head of Data. The same thread throughout: data systems that hold up when a real decision depends on them.
- Lead the Data function for the group, supporting 14 brands across four markets.
- Lead a 10-person multidisciplinary team spanning Data Engineering, Analytics Engineering, Analytics, BI and Data Science / Product Science — including hiring, career development and the operating model.
- Own the platform architecture and stay hands-on in it: AWS, Apache Iceberg on S3, Athena/Trino, Prefect, dbt and Superset — 500+ dbt models and 40 source systems under a 3h freshness SLA.
- Built governed metrics and semantic modeling into the platform — 300+ governed metrics serving 300+ internal BI users through a production semantic layer.
- Working on agentic analytics: giving AI agents governed, semantically described access to enterprise data over MCP, instead of relying on direct, ungoverned warehouse access.
- Introduced Analytics Engineering and BI-as-Code, turning analytics and BI into reusable data products the team can evolve without breaking what is already in use.
- Established a cross-functional product-analytics practice, shifting Data from a reactive reporting service to co-creation with Marketing, Sales, Finance, Product and academic operations.
- As Data Platform Manager (2023 – Oct 2024), led a team of 7 — four Data Engineers and three Analytics Engineers — covering Data Engineering and Analytics Engineering.
- Led the dbt adoption project, moving analytics onto a modular, layered data model that could scale with the business instead of accumulating one-off SQL.
- Rebuilt the Looker implementation on top of a governed, universal dbt data model — reducing coupling and improving scalability.
- Built and modeled the analytics platform on Redshift, then Snowflake, orchestrated with Airflow on Kubernetes; SQL and Python day to day.
- Joined post-acquisition and contributed to integrating CoverWallet's data platform and reporting into Aon.
- Led technology and product engineering as CTO (2018–2021), building data-driven products used in agronomic field operations.
- Worked on data platforms and distributed data technologies during the early enterprise adoption of Big Data.
- Selected for IBM's Spark 40 in 2015, a European specialist cohort focused on Apache Spark during its early enterprise adoption.
Ideas
The manifesto
Companies don't have a data problem. They have a decision problem.
Why the next wave of AI won't save organizations that still can't decide — and what to build instead.
The essay
The hard part of agentic analytics isn't the agent
Why Text-to-SQL is the easy part, and semantic systems, constraints and evaluation are what make analytical AI trustworthy.
Education & training
Education
Programs & technical training
Let's connect.
Happy to talk about data organizations, Analytics Engineering, or what it actually takes to make analytics reliable enough for AI agents.