Data Engineering That Makes Data Reliable
We design, build and modernise the data foundation underneath your business - architecture, integration, platforms, migration, quality and governance.
Our Scope
What Data Engineering Services Cover
Data engineering services cover the systems that move, store, clean and serve your data: integration from source systems, automated pipelines, warehouses and platforms, migration off legacy stores, the controls that keep data trustworthy, and the datasets analytics and AI depend on.
Data strategy and architecture — how data should be structured, related and stored
Data integration and pipelines — getting data out of source systems, automatically and reliably
Data platforms and warehousing — warehouse, lake or lakehouse, sized for how the data will be used
Data modernisation and migration — moving off legacy stores without losing anything
Data quality and governance — the controls that keep data trusted and compliant
Analytics and AI readiness — datasets prepared for the tools that consume them
Why data engineering is a business foundation problem, not an IT project
Most organisations do not have a data problem in the abstract. They have four reports that disagree, a CRM the sales team has quietly stopped trusting, and a spreadsheet that one person maintains and nobody else understands.
The instinct is to buy a dashboard tool or an AI product. That upgrades the presentation layer while the foundation stays broken. The numbers are still wrong, they are just wrong faster, and in better fonts.
Data engineering is the layer underneath: getting data out of the systems that hold it, into a shape people can rely on, with controls that keep it that way. It is rarely what anyone wants to buy, and almost always what decides whether everything above it works.
When To Engage
When You Need A Data Engineering Partner
Most organisations reach this point for one of six reasons:
Reporting nobody trusts, the numbers disagree and no one can trace why
One person manually prepares the same data weekly, and the process leaves when they do
Analysts cannot answer their own questions without raising a ticket
Your cloud data bill is growing faster than the value you get back from it
A system is reaching end of life, or an ERP or CRM replacement is committed
An analytics or AI initiative has stalled because the data will not support it
These are symptoms, not specifications. Our data engineering services start by establishing which pillar is causing the problem.
Common Obstacles
Challenges & Prevention
These are the five failure modes we see most often, and the controls we put in place against each.
Data scattered across systems never designed to talk to each other
We inventory every source during discovery and build integration once, properly, rather than adding another point-to-point connection to the pile.
Manual data preparation only one person understands
We automate the pipeline and document the transformation logic, so the process survives that person taking a holiday or leaving.
Reports that disagree with each other
We define reconciliation rules and a single validated source of truth, so a number can be traced to source rather than defended by whoever built the report.
Platform costs that climb faster than the value returned
We model storage and compute against your actual usage before build, and set monitoring on spend, so cost is a design decision rather than a quarterly surprise.
Committing to a platform before knowing it fits
We prototype the use case on free tooling first, so the platform decision is made against your own data rather than a vendor demo.
Our Methodology
Our Data Engineering Process
Prototype
Days - 2 WeeksWe build a working prototype of your highest-value use case on free tooling, using a sample of your real data. No licences, no platform commitment, no programme.
Deliverable: Your own data in the target shape, and an evidenced answer on whether the platform you were considering is the right one.
Assess
2-4 WeeksWe review what data you hold, where it lives, what condition it is in, and the specific business questions your data cannot currently answer.
Deliverable: A documented current-state view, a source system inventory and a data quality report.
Design
2-4 WeeksWe produce the target data architecture, the tooling decisions and the delivery sequence, with cost implications stated before anything is committed.
Deliverable: A target architecture, a phased plan you can budget against, and a modelled running cost.
Build & Operate
6-12 Weeks To First IncrementEach increment is usable on its own rather than a single platform reveal at the end. We hand over with monitoring, alerting and documentation, or run it for you.
Deliverable: Working pipelines in production in stages, and a platform someone is accountable for.
Types of data engineering we handle
Our data engineering services span six pillars. Most engagements start in one and expand as the foundation improves.
Data strategy and architecture
We map what data exists, define how it should be structured, related and stored, and produce a data architecture that matches how you actually operate rather than a diagram from a vendor deck.
Data integration and pipelines
Integration from applications, databases, REST APIs, SaaS platforms, files and IoT sources, then ETL and ELT pipeline development to transform, clean and enrich on the way through, with orchestration, error handling, testing and monitoring built in from the start.
Data platforms and warehousing
Warehouse, lake or lakehouse, designed for how the data will actually be used. As a Databricks partner we build lakehouse platforms where the workload justifies one, and conventional warehouses where it does not, across Azure, AWS and Google Cloud.
Data modernisation and migration
Legacy databases, CRMs, ERPs, file systems and SharePoint estates moved to modern platforms, with reconciliation rules agreed before a single record moves, phased cutover, and rollback at every checkpoint.
Data quality and governance
Validation, cleansing, deduplication and reconciliation rules, with automated monitoring so issues are caught by the system, not by a person reading a report. Lineage and metadata mean any number can be traced to its source.
Analytics and AI readiness
Curated datasets for BI and reporting tools, feature and training data for machine learning, and retrieval-ready document stores for generative AI, and the honest answer if your data cannot yet support the use case you have in mind.
Platform Choice
Warehouse, Lake, or Lakehouse
Choosing between the three is the decision most organisations get wrong, usually by buying the most capable option rather than the right one:
Data Warehouse
If you need
Structured reporting and BI, answering questions you already know you will ask
Why
Optimised for fast, repeatable queries over clean, structured data. Cheaper to run and simpler to govern.
Data Lake
If you need
To hold large volumes of raw or mixed-format data cheaply, before you know what you will ask of it
Why
Low storage cost and no schema commitment up front. Weak on query performance without a layer above it.
Lakehouse
If you need
Both, on one platform, with analytics and AI running on the same governed data
Why
Warehouse performance and governance over lake-scale storage. This is where Databricks fits, and where most AI-ready foundations end up.
Platforms and tools we work with
lakehouse platforms, Delta storage, Unity Catalog governance and analytics workloads built on Databricks
Microsoft Azure, AWS, Google Cloud
Microsoft Fabric, Azure Data Factory, Azure Synapse, Power BI, SharePoint and Microsoft 365
SQL Server, MySQL, PostgreSQL, MongoDB
Laravel and AWS, MERN, .NET, native iOS and Android
We resell none of these, so the platform gets chosen for your workload and your budget rather than for our margin. Being a Databricks partner is also why we will tell you when you do not need Databricks - if your volumes do not justify a lakehouse, a warehouse costs less to build, less to run and less to govern, and that is what we will scope.
Security, compliance and governance
Regulated data needs the same governance in a pipeline as it has at rest. That is designed in from the first phase, not added once someone asks about it.
UK GDPR obligations applied to data in transit through pipelines, not just to source and target systems.
Data residency agreed and evidenced before data moves between environments.
Encryption in transit and at rest, with access to data environments restricted and logged.
Role-based access controls across the data estate, reviewed as the platform grows.
Retention policies and audit trails, retained and ready for review.
Value Delivered
What You Get
What data engineering services leave you with:
Data your teams trust, because the rules that validate it are defined and monitored rather than assumed
Reporting that reconciles, so leadership stops arbitrating between conflicting numbers
Automated pipelines that remove manual data handling and its errors
A platform sized for your actual usage, with the running cost known in advance
Governance and audit trails ready for compliance review
A data foundation that supports analytics and AI without being rebuilt first
How long data engineering takes, and what drives the cost
A prototype takes days to a couple of weeks. An assessment takes two to four weeks. The first useful increment typically follows within a further six to twelve weeks, because we deliver in usable stages rather than one final release.
— integrating five is harder than five times one, because the rules must resolve conflicts between them
— duplicates, gaps and inconsistent formats are found in discovery and fixed before they reach the platform
— a warehouse costs less to build and run than a lakehouse, which is why we size the decision rather than default to the larger option
— handover to your team or support from ours, decided at design stage
We scope all four during the assessment, so the estimate you get is based on your actual usage rather than an average.
FAQ
Frequently Asked Questions
Everything you need to know about working with 200OK Solutions.
Data engineering builds the systems that collect, move, store and clean data. Analytics uses that data to answer questions. Analytics depends entirely on engineering being done properly first, which is why dashboards built on a weak foundation produce confident answers that happen to be wrong.
Warehouses suit structured reporting on known questions. Lakes suit large volumes of raw mixed data held cheaply. Lakehouses give you both on one governed platform. Many organisations need only a warehouse, and the prototype will show you which before you buy anything.
Yes, and we would rather you asked. We build a working prototype of your highest-value use case on free tooling using a sample of your real data. You see the result before any licence, platform decision or programme is committed to.
No. The partnership means we can build a lakehouse properly when one is justified, not that every problem needs one. We resell no platform, so if your volumes suit a warehouse that is cheaper to build, run and govern, the prototype will show you that before you commit.
No. Assessing and cleaning the data is part of the work. Discovery identifies duplicates, gaps, orphaned records and format conflicts, and we agree what gets fixed, merged or left behind. Cleaning it yourself first is rarely time well spent.
A prototype takes days to a fortnight. An assessment takes two to four weeks, and the first useful increment typically follows within six to twelve weeks because we deliver in stages. Larger programmes run longer, but value should arrive well before the end.
Governance is designed in from the first phase, not added afterwards. That covers access controls, data lineage, retention policies and audit trails, applied to data moving through pipelines as well as at rest. Data residency is agreed before anything moves.
Yes. We hand over with documentation and monitoring in place if you want your team to run it, or we provide ongoing support and pipeline operations if you would rather we did. Both are scoped at design stage so the running model is not an afterthought.
See it working on your own data first
Tell us the question your business keeps failing to answer with confidence. We will prototype it on your data before you commit to a platform, a licence or a programme - and if the answer is that you need less than you thought, we will tell you that too.