My story

I am a veteran data engineer. I help companies raise their data innovation capabilities beyond data warehouses, lakehouses, analytics, and prototypes, to enable robust data and AI products. My work is founded in the data architecture and operational patterns that we pioneered at Spotify, which became a key factor in the company’s success.

Early DataOps and platform engineering

In 2013, I joined Spotify’s core data team where we built and rolled out a unified data platform that democratised data for both analytics and product features. We pioneered continuous deployment, statistical process control, and other operational processes in data lake environments—practices that later became known as DataOps.

Most contemporary platforms were enforcing and strongly prescriptive, but we instead built a platform and data engineering process around documented guidelines and incentives, inspired by the success of Google’s bottom-up process improvement program “Test Certified,” which I had been part of a few years earlier. The concept of an enabling, documented, incentive-based process spread within the company, was named “Golden Path,” a term that eventually became a core platform engineering concept and spread to other companies—including Google, closing the circle. I also wrote the first draft for Spotify’s own Test Certified program, which later was forked to a “Test Certified for Data”.

The team behind Discover Weekly, Spotify’s most popular AI feature, later attributed their success to the fact that “the company had empowered bottom-up innovation” — which is what we aimed to achieve with the enabling platform and process. The first usable version of Discover Weekly was built in a few weeks.

Few Scandinavian companies can match this level of innovation with data and AI. My aim is to spread these capabilities beyond a small elite of technical leaders.

A lightweight playbook

After Spotify, I set out to replicate that innovative capability in smaller environments. At Bonnier News, I led construction of a common data platform with a different approach: minimal technical investment, removing process waste, and adopting agile practices driven by use cases. We had satisfied customers after weeks and return on investment after months.

I have used this architecture in every platform since. One distinctive feature is automated end-to-end pipeline testing—something we recognised as important at Spotify but never implemented.

Track record

I have led the development of 9 data platforms since 2013, in media, retail, IoT, construction, healthcare, and telecom. Notable engagements include:

Consumer products With 1.5 developers over 3 years, we built all the company’s user-facing offline data processing—162 pipelines producing 700 datasets/day. We averaged 80 commits and 35 production deployments per month, and operated 8 Kubernetes clusters at 15–20 KEUR/month—a fraction of the cost of the existing legacy solutions, which were built by a larger team with contemporary technology from major providers, but found inadequate for operational data processing.

Retail With 1–3 developers over one year, we built 70 pipelines producing 3,700 datasets/day covering transactions from e-commerce and physical stores, procurement, and demand forecasting. Cloud costs were 2–3 KEUR/month.

Telecom With 2–5 developers, we built a data platform and ca 100 pipelines for SMS route optimisation, financial reporting, and malice detection (fraud, phishing, etc) with machine learning, producing 12000 datasets/day at cloud costs below 10 KEUR/month.

Construction Primary technology provider in a three-year method innovation effort. Out of approximately 5 tools and data-driven approaches tried, one had massive measurable financial impact. Construction projects using the tool improved margins by several percent, a significant number in a vertical with small margins.

Media Founded a team that built financial data processing pipelines to strengthen compliance and financial and operational risk management in preparation for one of the largest initial public offerings among Swedish startup unicorns.