SCHIPHOL DATA PLATFORM

Gathered requirements from multiple Data Product teams and documented user stories on the backlog in collaboration with the Product Owner Platform Technology & IoT

Developed an integrated implementation plan covering DPI expansion, inner sourcing rollout, and team onboarding across four phases.

Built new Terraform modules and implemented Databricks Asset Bundles (DABs) as the declarative automation standard across the department — enabling teams to define jobs, pipelines, and ML workflows as code with consistent, reproducible deployments

Rolled out DABs adoption across multiple Data Product teams — providing templates, documentation, and hands-on onboarding to replace ad-hoc deployment methods with governed declarative pipelines

Enabled AI and ML workloads on Databricks — configuring GPU clusters, MLflow tracking, and Feature Store for data science teams across the organisation

Established inner sourcing of Terraform scripts — making infrastructure code available in the Schiphol code repository for self-service adoption by teams outside Data & Analytics

Co-implemented Databricks Security Analysis Tool (SAT) across the platform — providing automated security posture assessments, misconfiguration detection, and best-practice compliance reporting for all Databricks workspaces

Automated security assessment workflows — including SAT-driven compliance checks, vulnerability scanning, and policy-as-code validations — integrated into CI/CD pipelines to enforce security standards across all data products

Owned monitoring and maintenance of all Databricks workspaces across Schiphol — including cluster health checks, job failure alerting, workspace configuration audits, cost tracking, and proactive capacity management to ensure platform stability and performance

Built further infrastructure automations for environment provisioning, configuration drift detection, and automated remediation to reduce manual operational overhead

Coordinated with the Technical Solution Architect and DevOps Automation team to ensure alignment on infrastructure standards, security baselines, and deployment patterns

Data Product and AI teams actively adopting the Data Product Infrastructure for their products, with clear onboarding paths and documentation

Databricks Asset Bundles (DABs) established as one of the standard deployment models — teams deploying jobs and pipelines declaratively with full version control and CI/CD integration

Databricks AI/ML workspaces operational — enabling data science teams to develop and deploy models with governed infrastructure

Terraform source code available in the Schiphol repository — enabling inner sourcing and self-service infrastructure provisioning across the organisation

Databricks Security Analysis Tool (SAT) providing continuous security posture visibility and automated compliance reporting across all workspaces

Automated security assessments reducing manual compliance review effort and ensuring consistent policy enforcement across data products

All Databricks workspaces continuously monitored and maintained — with proactive alerting, regular health checks, and zero unplanned platform downtime

Comprehensive documentation and process definitions published in Confluence, ensuring knowledge transfer and long-term sustainability

Bi-weekly progress updates delivered to Product Owner, with measurable adoption metrics tracked across teams