Platform development

I lead technical work on a lightweight, opinionated data platform using standard cloud and open source components. I guide teams on pipeline development, architectural decisions, and compliance.

I have developed a lightweight playbook to achieve data engineering efficiency that matches the leading tech companies, but with minimal technical investment. If this playbook is followed, my clients can expect > 10x improvement on key data efficiency factors in comparison to typical data pipeline development in data warehouses or lakehouses:

• Operating 100s of data pipelines per developer, not dozens
• 50–100 commits per developer per month, not 5–10
• First value in weeks, not months
• Return on investment in months, not years
• 1–10 KEUR/month cloud costs, not 100–1000 KEUR

This engagement mode is suitable for organisations with software engineering competence and ambition to evolve their data processing capabilities to build a foundation that enables sustained data and AI innovation.

Pipeline development

In companies with an existing data platform, I build solutions for data-powered features, complex analytics, or AI applications. I work within clients’ infrastructure and integrate with one of their data teams.

This works best for companies with mature DataOps practices or an ambition to develop them. I can contribute data and AI feature design, data pipeline development, data architecture guidance, and risk management processes for compliance and quality assurance.

Hosted custom data pipelines

For companies where software or data engineering is outside core business, I develop and host custom data processing as an external service.

The custom data pipelines run on Scling’s data platform Orion, which is designed to avoid lock-in. The platform and data pipelines are portable between clouds, built on open source components, and designed to allow handing over operations to another supplier if desired.

This engagement mode provides data and AI capabilities without requiring clients to build and maintain digital infrastructure.

Business process automation

The companies that are most successful with data and AI today started with automating their chores and business processes. Data engineering, amplified by AI agents where applicable, is more cost-efficient for automation than robot process automation (RPA) or commercial tools, and it is more reliable than AI agents alone.

This mode is applicable for any company and yields swift return on investment.

Strategy and advisory

For companies shaping their data and AI strategy, I offer advice, presentations, and workshops on the following topics:

• Realising data and AI value in practice
• Data architecture and operations
• Organising teams for data success
• Privacy and compliance without compromising innovation and velocity

See the list of conference presentations for further potential topics.

My engagements often start with strategic or advisory work, but I primarily provide advice when there is potential for a future implementation engagement.