Ruben Morgenland.
Platform engineer & gadgeteer. I design and build data platforms that move teams forward from raw data to reliable tools. Beyond the technical side, I care about how people actually work with data and how processes can be made smarter. Outside of work you'll find me tinkering with the latest gadgets or playing hockey.
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Skills
The tools and technologies I use to build reliable, scalable data platforms.
Work Experience
A record of roles, responsibilities, and the systems I've built along the way.
Data Engineer with 6+ years of experience spanning the full data lifecycle from on-premise systems to cloud-native architecture. I started my career as a BI Consultant, delivering board-level reporting and client-facing dashboards in MicroStrategy, before moving into data engineering with more end-to-end perspective. I understand data not just as pipelines and models, but as something business stakeholders depend on to make decisions.
Today, I own critical infrastructure across the full data stack. I’m the subject matter expert on our on-premise data warehouse, the person colleagues turn to for troubleshooting and system knowledge. While building its successor: a GCP BigQuery platform modeling Data Vault 2.0 across roughly 3,000 CDC-ingested source tables. I designed and built custom Python frameworks that standardize ~80 client configurations and move data reliably in and out of our warehouse via SFTP, S3, and secure email, and I orchestrate the entire platform, from ingestion to dbt transformations, using Dagster.
My proudest work was leading the data platform behind ticketing operations for the **Paris 2024 Olympics** — starting as the sole engineer on the initiative and scaling it into a team effort as the Games approached. The system processed over 20 million transactions and supported 1.6 million+ visitors, and the architecture I designed became the reusable foundation for the following Winter and Summer Games. Proof that good data engineering outlasts the event it was built for.
I care about systems that are reliable under real pressure, standards that make a codebase maintainable by more than just me, and translating business needs into models that actually hold up.
Tijdens deze afstudeerstage heb ik de regierol van een beheerteam binnen DICTU versterkt. Door rapportages op basis van systeeminformatie automatisch te laten generen is het team in control op de beheer/ontwikkel omgeving. Met hulp van de tool Splunk heb ik systeeminformatie verzameld, geanalyseerd en gerapporteerd. Met deze verbetering is het rapporteren volledig geautomatiseerd en is het beheer sterker in control op de beheer en ontwikkel omgeving.
Een optimale ondersteuning van de GRZ met Careware; een onderzoek naar de best practices voor de bedrijfsprocessen en inrichting van Careware voor de GRZ.
Projects
Selected work — from large-scale reporting platforms to open infrastructure tooling.
Contributing to the build-out of a new cloud data platform on GCP BigQuery designed to replace the legacy Exasol data warehouse, working within a dedicated team. Building the raw vault across ~3,000 source tables ingested via CDC through GCP Datastream, modeling from staging (layer 1) through to a consumption-ready business vault using Data Vault 2.0 methodology.
I led the delivery of data products, infrastructure, and integrations that powered real-time operational ticketing insights for one of the world’s largest sporting events.
Our platform covered the entire customer and operational flow:
By combining high-availability pipelines and optimized data models, we ensured operational and strategic teams had the right insights at the right time to anticipate and solve challenges instantly.
Data Engineering highlights:
Seeing these systems in action during the Games—and knowing they played a direct role in making the event a success was ab experience I will never forget.
Built a reusable Python import framework (orchestrated with Dagster) to ingest data from ~6 distinct source systems. Including the payment service provider, customer service platform, and marketing tools (Google Ads, Meta Ads) via both flat file transfers and API-triggered download hooks. Designed with automatic retry-on-failure and layered error handling in Dagster, backed by dbt tests and source-level checks for data validation once loaded. Built as a net-new capability with a reusable codebase that reduced the effort needed to onboard additional sources.
Orchestrated the entire data platform using Dagster, including import jobs, export jobs, dbt tests, and dbt incremental models as a single, observable pipeline ecosystem. Implemented event-driven triggers (for example a sensor listening for the PSP’s API webhook to automatically kick off the relevant import job) rather than relying purely on fixed schedules. Improved platform reliability by reducing pipeline failures and manual re-running, speeding up incident detection, and centralizing pipeline visibility and monitoring through the Dagster UI.
Designed and built a custom Python framework (originally prototyped in PHP) for automated data distribution from the enterprise data warehouse to external partners via SFTP/FTP, S3, and encrypted email — replacing a patchwork of manual exports and one-off per-client scripts. Standardized and now maintains ~80 active client configurations, supporting delivery frequencies from 15-minute intervals to weekly batches and file sizes up to 10GB, with an architecture built to extend easily to new destination types. Built and owned largely independently.
Education
Get in Touch
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