My name is Lars Albertsson and I am a veteran data engineer. I was in the team that pioneered DataOps and data platform engineering at Spotify, enabling the data democratisation that made the company one of the leaders in data- and AI-powered features. Since then, I help companies raise their data innovation capabilities and maturity beyond data warehouses, lakehouses, analytics, and prototypes, to robust data and AI products. I use a lightweight playbook founded in the data architecture and operational patterns that we evolved at Spotify, but achieving the same efficiency and development speed with minimal technical investment.

The software engineering field got disrupted when agentic development became useful in 2025/2026. The new possibilities tie perfectly in with the development automation work I started in 2021 – automating the data and software engineering lifecycle with developer bots executing in harnesses with guardrails, holistic quality assurance, and automated evaluation. Today, my main passion is combining my data engineering playbook with AI coding agents in a data pipeline factory, where agents build, test, and evaluate pipelines from specifications.

Read more about my background and my journey from productivity engineering at Google, via democratising data at Spotify, to data pipeline software factories.

Clients

Raising data and AI capabilities

Companies that want to move beyond analytics or prototypes to operational production-quality data and AI products.

Seeking data/AI maturity

Companies that want to modernise and structure existing ad-hoc, microservice, or data warehouse based data processing.

Already mature, need capacity

Companies with a mature data platform that need experienced engineers to build data and AI features.

Data or AI features as a service

Companies that want to build data or AI features, and have them hosted and operated as a service.

Services

Platform development

Leading a small team to build up data capabilities and a data platform.

Pipeline development

Building data processing pipelines in client's existing platform.

Hosted custom data pipelines

Building and operating data-powered or AI features, hosted externally or in client's infrastructure.

Business process automation

Automating repetitive business tasks with robust data engineering.

AI-powered pipeline factory

AI agents that autonomously build, test, and evaluate data pipelines from specifications, with the harness to make it robust.