Dear Roosmarijn,
I was excited to see the Commercial Tech & Analytics Lead opening at FrieslandCampina Ingredients and wanted to reach out directly. With my background in data engineering, analytics, and AI, I see a strong alignment between what I have built and what this role needs.
I should be honest — I do not have 12 years of MarTech leadership. What I bring is three years of corporate data engineering at RoyalHaskoningDHV and a year at FrieslandCampina leading AI initiatives in Global HR People Analytics. I know this organisation's data landscape from the inside, and I have a clear picture of where the commercial data challenges are and how to address them.
At RoyalHaskoningDHV, I owned the ETL layer that fed Power BI dashboards across the organisation. I led the migration of the corporate data lake to Parquet format, reducing Azure storage costs by nearly 90% while improving query performance. Some of the dashboards I built covered HR processes like performance evaluations and salary reviews. I also worked closely with the CRM owner to build data models for a Zipper Analysis dashboard that mapped our organisation's connections to customer organisations — giving me direct exposure to commercial data modelling and cross-functional CRM collaboration. I have hands-on experience with Power BI, SQL, Python, Alteryx, and Azure, which maps well to the data platform ownership this role demands.
At FrieslandCampina, I work with PII-sensitive HR data where governance is not optional. I led the migration of HR DataMarts to Azure and explored locally-hosted open-source LLMs for privacy-conscious AI use cases where cloud-based solutions were not viable. I am also developing a Databricks Genie-based HR analytics assistant that lets stakeholders ask questions in plain language and get answers from live data — currently in PoC with 70% accuracy on test queries. The adoption pattern I am seeing — people who try it want it for their own teams — is exactly what this role needs to drive for Salesforce and commercial data.
Beyond my professional experience, I work extensively with AI agents in my personal development workflow — using them for code review, research automation, and pipeline debugging. These projects have deepened my understanding of what AI can and cannot do in an enterprise setting, which I believe is directly relevant to driving AI adoption across Ingredients' commercial teams.
What excites me about this role is the opportunity to move from building data systems to owning the commercial technology and data strategy. I know FrieslandCampina's data landscape, I know which teams have clean data and which ones are guessing, and I know how hard it is to get different functions to agree on what a qualified opportunity means. I want to move to the side where I can fix those problems rather than just analyse them.
I have also spent time thinking about what I would actually do in the first 90 days in this role. I would start with a CRM health check — data duplicates, integration failures between Salesforce and ERP, and adoption rates by region. Then redesign the opportunity stages to reflect the actual ingredient buyer journey: sample request, plant trial, contract negotiation, volume commitment. By day 90, I would publish a data quality scorecard by region and pilot Einstein Activity Capture on a few power-user inboxes to auto-log emails and meetings. The principle is simple: build trust in the data first. Without that, no AI strategy or automation roadmap will land.
I would welcome the opportunity to discuss how my experience in data engineering, AI implementation, and stakeholder collaboration aligns with the commercial technology vision for Ingredients. Thank you for your time and consideration.