Data & AI specialist from 2019–2024, contributing as the team scaled from a smaller specialist setup into a larger organization.
Ex-Databricks · AI Transformation & Product GrowthAmsterdam / Working globally
The positioning / 01
AI transformation needs a product, a market and a system that can operate.
Founders often face the same connected problem: the AI use case is promising, but the product wedge, customer evidence and route to distribution are still unclear.
I connect product strategy and growth execution with the technical depth required to implement AI agents, automation and reliable data systems—not just advise from the sidelines.
Experience with Infosys Limited, Cognizant and Wells Fargo US across enterprise engineering, reliability and data platforms.
Multi-cloud SRE and infrastructure reliability work for major financial organizations during 2018–2019.
A foundation across AI, data, cloud, SRE and DevOps now applied to product launch, PMF and growth automation.
AI transformation / In motion
Make the complex clear enough to move.
Product strategy, technical architecture and distribution work best when they are explained as one operating system—not three separate conversations.
Meet the founderWhat I help founders build / 02
Explore all services From AI use case to product, pull and repeatable growth.
AI agents + transformation
Identify practical AI use cases and design agents, workflow automation and human-review systems that can move from proof of concept into operations.
- AI agents
- Automation
- PoC
Product launch + PMF
Help founders sharpen the problem, define the first product wedge, structure market evidence and learn toward product-market fit.
- Launch
- Positioning
- PMF
Distribution + GTM automation
Design repeatable distribution, sales and marketing workflows supported by AI agents, automation and clear feedback loops.
- Distribution
- Sales
- Marketing
AI + data foundations
Build the data, cloud, integration, reliability and DevOps foundations required for AI-focused products to operate at production quality.
- Databricks
- Cloud
- SRE
Selected experience / 03
Portfolio & experience Complex environments. Practical implementation.
Financial services / Netherlands
Multi-cloud reliability
Applied SRE, observability, infrastructure automation and secure delivery practices across a major financial environment.
Azure / AWS / GCPEnterprise platforms
Observability + incident intelligence
Connected application and infrastructure signals to improve root-cause analysis, proactive detection and structural remediation.
AppDynamics / Splunk / ELKData engineering
Large-scale data migration
Supported governed migration from relational databases to distributed data processing with batch workflows and reliability controls.
Hadoop / HDFS / MapReduceTechnology evidence / 04
Tools are supporting proof—not the strategy.
Experience across enterprise application stacks, multi-cloud platforms, observability, data engineering and secure delivery.
AI + data
- Databricks
- Hadoop
- HDFS
- MapReduce
- Power BI
- Python
- SQL
Cloud + platform
- Azure
- AWS
- GCP
- Terraform
- Ansible
- Docker
- Kubernetes
Observability
- Splunk
- ELK
- Dynatrace
- New Relic
- AppDynamics
- Grafana
Engineering + security
- Java
- Spring Boot
- Jenkins
- Azure DevOps
- SonarQube
- Fortify
- OAuth
The approach / 05
“Start with the operational problem. Design for production. Measure what changed.”
Clear scope, maintainable implementation, documentation and knowledge transfer—not an impressive demo that cannot be owned.
About the experience