A production-ready, custom data engineering platform built around your sources, users and governance requirements. Ingestion, storage, transformation, quality and consumption as one coherent whole, rather than loose ETL scripts and tools nobody maintains any more. We build on managed components where we can, and write custom code where we must.
A data engineering platform is something different from a few ETL scripts or a handful of API integrations. It is the complete foundation that carries your data from source to end user: reliable, repeatable and traceable. Where data integration consulting is about advice and building integrations between systems, and a real-time analytics platform focuses on streaming analysis, data engineering covers the pipelines, storage, orchestration and quality monitoring underneath.
We build data platforms for organisations that want to consolidate their source data into a data warehouse or lakehouse, replace their overnight batch jobs with a managed platform, or lay a foundation for analytics, machine learning and reverse ETL. No off-the-shelf package, no lock-in to a single vendor: a platform that fits your sources, scale and team.
The result is an environment in which new data sources land predictably, transformations are version-controlled, quality errors become visible before a dashboard displays them, and your analysts and data scientists work with data the business can trust.
In practice we see three main types of request: a scale-up without an existing warehouse that wants to start from scratch, an organisation with loose Python scripts and cron jobs that no longer scale, and an internal team that needs a reliable back-end dashboard on fresh data from multiple systems. For all three we build on managed components, choose the stack that suits your team size and budget, and make sure your people can ultimately manage it themselves.
We have worked for years with organisations that have outgrown Excel and standalone BI reports. For logistics companies, B2B SaaS businesses and operational teams in manufacturing and finance. Always with the same principles: choose managed where possible, write code where necessary, and make sure the platform stays readable for whoever has to build on it tomorrow.
Depending on where your organisation stands: a platform built from scratch, a migration from loose scripts to a managed platform, or a focused back-end dashboard with fresh data from all your systems. In the first conversation we determine which project fits.
For scale-ups and organisations without an existing data warehouse. We set up ingestion from your SaaS tools and databases, choose a lakehouse or warehouse suited to your scale, model the transformations, and put orchestration and monitoring in place. Delivered production-ready, with your team brought along for ongoing management. Typical scope: ingesting a handful of source systems, a staging and marts layer in dbt, one or two primary use cases live, and the governance foundations for further expansion.
For organisations with existing pipelines that no longer scale: cron jobs on standalone VMs, Python scripts without version control, unreliable overnight batches. We document the existing flows, rebuild them on a managed platform, and run both in parallel until we have confidence in the new pipeline. Only then do we switch over the downstream consumers, so dashboards and operational tools are never left without data.
An internal dashboard for your operations, finance or management team, fed by a lightweight data pipeline that brings together data from multiple sources and keeps it up to date. Not a Power BI report on an Excel export, but a small platform with its own ingestion, models and front end. Suited to a single focused question (margin per customer, stock rotation, OEE overview) for which BI tooling is overkill, but Excel has become unreliable.
Not just pipelines that move data, but a complete data environment that your team can extend and manage itself.
Four common situations in which clients come to us. If one of them sounds familiar, we'd be happy to talk further.
You're growing fast and the ad-hoc dashboard running on a production replica no longer holds up. You need a proper platform — ingestion, modelling, quality — before the whole organisation starts relying on the same Looker views and nobody remembers how the definitions came about.
Python scripts on standalone VMs, cron jobs without retries, nightly batches that sometimes fail to run. You want to move to a managed platform with versioning, alerts and lineage, without losing the existing flows or leaving the business without data in the meantime.
HubSpot, Salesforce, Stripe, NetSuite, Zendesk, Mixpanel, production database — all with their own customer ID and their own definition of "active". You want a single view of the business, with clear definitions, governance and ownership for each metric.
You want to write scores, segments or predictions from the warehouse back into HubSpot, Intercom or your own app. Or a feature store for ML models running in production, with versioning and monitoring so data drift is spotted in time.
For most use cases batch is fine, but one or two processes need fresh data within seconds. You don't want to run two parallel platforms — we combine batch and streaming on a single stack, with clear patterns for when each path is used.
A team — finance, operations, customer success — needs a dedicated dashboard that is always up to date and combines data from several systems. Excel exports and BI tools fall short; you want something that runs in a browser, with authentication and roles, fed by a lightweight pipeline.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →A conversation in which we establish which sources you have, which use cases the platform must serve, and what your team looks like. We review existing pipelines, BI tools, data ownership and the pain points that prompted you to start this project. It produces an initial direction: greenfield, migration or a dedicated backend dashboard.
A workshop with your data and IT team. At the end you have a reference architecture: which ingestion tools, which warehouse or lakehouse, which orchestration and quality framework, and which catalogue. Including a reasoned yes or no on alternatives, so you know not only what we choose but also why we don't choose the others. An initial plan and sprint roadmap are included.
Each sprint delivers a working slice: one source fully ingested, one use case modelled end-to-end, or one quality check running live. You can test along the way, and your analysts see data flowing in. Over a multi-sprint engagement, the core platform takes shape with the first use cases, built in thin vertical slices rather than months of "ingestion phases" with no visible results.
Production cutover, parallel runs where possible, documentation and hands-on sessions for your team. We write a runbook for the most common issues, a data dictionary for the marts layer, and an onboarding document for new analysts or engineers. Afterwards, optional ongoing management for security, monitoring and further development.
What clients usually want to know before we start.
A thirty-minute introductory call, with no obligation. We listen to your sources, use cases and current data landscape, and give direction you can act on, even if you ultimately choose a different route.
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