Data & Analytics
Data Engineering Services
Nobody actually wants a data platform. What you want is to ask a question, get an answer, and be believed when you repeat it in a board meeting. Data engineering is the work that earns that: pipelines that run, a warehouse built for analysis, and the quality checks and lineage that mean a challenged number has an answer rather than a shrug.
What Data Engineering means in practice
Who it’s for: Organisations that want to decide on their data with confidence rather than argue about whose spreadsheet is right, and teams whose reporting, analytics or AI ambitions are blocked by data that is scattered, slow, or not trusted enough to act on.
Most organisations’ data is scattered across systems that were never meant to be analysed together. The CRM knows about accounts, billing knows about invoices, the product database knows about usage, and each holds a slightly different version of what a customer is. That is why the dashboards nobody trusts and the reports that take a week both exist. Data engineering is the unglamorous work that fixes it: pipelines, a warehouse, models and quality controls that turn scattered operational data into something a business can decide on without an argument about whose spreadsheet is right.
The point of all of it is trust, and trust is harder to build than throughput. Getting data from one place to another is not especially difficult; there are managed connectors that will do much of it for you. What is difficult is knowing the number on the dashboard is right, that it means the same thing this month as last, that it will still be right tomorrow when the source quietly adds a column, and that when someone challenges it you can trace it back to the row it came from. A platform that moves data quickly but cannot answer those questions is not an asset. It is a faster way to be confidently wrong.
This is the pillar of our data work, and it is worth placing against the more specific services beneath it. ETL development is the movement: the pipelines, their orchestration and their reliability. Data warehouse development is the destination and how it is modelled. Big data covers the techniques you reach for when volume or velocity breaks ordinary tooling. Data management is governance and stewardship as an ongoing discipline. Business intelligence is the consumption layer people look at. Data engineering is the whole system and, more usefully, the judgement about which of those parts you genuinely need, in what order, and which you should not build yet.
What you get
- A platform design argued from the questions you actually need answered, so the modelling, refresh cadence and storage choices follow from real decisions rather than from a reference architecture diagram
- Ingestion from your real sources (operational databases, SaaS APIs, files, event streams), reading incrementally and without putting analytical load on the systems your business runs on
- A warehouse or lakehouse modelled for analysis and kept separate from production, so a heavy query written by an analyst cannot slow the application your customers are using
- Transformations as version-controlled, tested code (typically dbt), with the business definitions written down once rather than reimplemented differently in every dashboard
- Data quality tests and freshness checks that fail loudly in the pipeline, plus lineage you can follow from a figure on a report back to the source row that produced it
- Monitoring on the failures that matter: late and failed runs, volume anomalies, upstream schema drift and stale tables, routed somewhere a human will see them
- Cost treated as a design constraint, because warehouse compute is billed by use and an unexamined platform gets expensive quietly, plus the documentation, runbooks and handover that let your own team operate it
What Data Engineering does for you
The end of duelling spreadsheets
The most expensive symptom of poor data infrastructure is not slow reporting, it is the meeting where two teams present different numbers for the same thing and the next half hour goes on working out which is right. That happens because each team extracts its own data and applies its own definitions. One modelled warehouse with the definitions agreed and implemented once removes the cause rather than the symptom, and people go back to debating what to do about the figures, which is what you were paying them for.
Production stops paying for analytics
Reporting straight off the operational database is the default because it is the path of least resistance, and it works right up until it does not. An analyst writes an unbounded join, the database starts swapping, and the application slows for real customers. Moving analytical workloads onto a warehouse built for them removes the risk entirely: heavy queries run on infrastructure designed for scanning large volumes, the production system serves the application, and the two stop competing. Both get faster, from the same piece of work.
Cost that tracks change, not size
A naive platform reprocesses everything every night, which is fine at a million rows and painful at a billion. The nightly run creeps from minutes to hours until it stops finishing before people need the data, and because modern warehouses bill for compute, the invoice grows on the same curve. Incremental models, partitioning, clustering and change data capture where the source supports it mean the platform processes what changed rather than what exists: the difference between infrastructure that scales with your growth and infrastructure you rescue every time the data does.
Why teams choose us for Data Engineering
- You have been burned by a data project that produced impressive architecture and no trusted numbers, and you want people whose first question is which decisions this must support rather than which tools to buy.
- You need the honest scoping conversation: whether you need a warehouse yet at all, whether a lakehouse is warranted or a straightforward analytical database would serve you for years, and whether streaming is a genuine requirement or an expensive preference.
- You want quality and lineage treated as the point rather than as the phase that gets cut when the timeline tightens, because that is what decides whether anyone believes the output.
- You want to own the result: pipelines, models and infrastructure in your cloud accounts and repositories, defined as code your engineers can read, with no proprietary layer of ours you must keep paying for.
What Data Engineering includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Platform architecture and the build order
Deciding what the platform needs to be and in what sequence to build it: warehouse or lakehouse, batch or streaming, which sources matter first, what can wait, and what should never be centralised at all. This is the highest-value part of the engagement and the part most often skipped in favour of standing up tooling. We make the call on your volumes, your team, your freshness requirements and your budget, and write down the reasoning so it can be challenged later by people who were not in the room.
Ingestion and change data capture
Getting data out of operational databases, SaaS platforms, APIs and file feeds cleanly and incrementally, without hammering the systems it lives in. Watermark-based reads where the source has reliable timestamps or sequence keys, log-based change data capture with tools such as Debezium where the database supports it and the requirement warrants it, and managed connectors like Fivetran or Airbyte where renting one beats maintaining a bespoke API client for a commodity source.
Warehouse and lakehouse engineering
The destination, built for analysis rather than for running an application. Dimensional models where the questions are well understood, wider denormalised tables where query patterns favour them, and deliberate handling of history so a customer who changed segment last March does not silently rewrite last year’s reporting. We work across Snowflake, BigQuery, Redshift, Databricks and Postgres-based analytical setups, and with open table formats such as Apache Iceberg and Delta Lake.
Transformation and the semantic layer
Turning raw source data into the modelled, documented tables people report on, as version-controlled and tested SQL rather than logic buried in a scheduled script or, worse, inside individual dashboards. This is where dbt earns its place: transformations in source control, tested, documented, with lineage generated from the code itself. Above it sits the semantic layer, your core metrics defined once so revenue means the same thing in the finance report and the sales dashboard.
Data quality, testing and observability
Checks that run inside the platform and fail loudly rather than importing problems and leaving you to find them in a board pack. Freshness tests that catch a stale source, volume anomaly checks that catch a partial load, uniqueness and referential tests that catch structural breakage, and business-rule assertions that catch data which is technically valid and obviously wrong. Tools such as dbt tests, Great Expectations or Soda have their place, but the tooling matters less than deciding, per dataset, what wrong looks like and making the platform shout when it sees it.
The data foundation for AI and machine learning
Most AI projects stall because of data, not models. We build the layer underneath: clean, joined, documented datasets with history preserved so a model trains on what was known at the time rather than on figures since restated, feature pipelines that compute identically in training and in production, and the retrieval and embedding infrastructure grounded language-model features depend on. Where a client arrives asking for AI, this is frequently the honest first project, and we will say so.
Where it fits
The board pack that takes a week
Monthly reporting is assembled by hand: three exports, a reconciliation, a set of pivot tables and a nervous check before it goes out. It consumes days of skilled time and produces figures nobody is quite confident in. We model the underlying data properly, implement the definitions once, and generate the pack from the warehouse. The week comes back, and the figures stop changing depending on who prepared them.
Analytics that is slowing down the product
Reporting queries run against the production database because that is where the data is, and the application has started to suffer for it at month-end. We stand up a warehouse fed by incremental pipelines and move the analytical workload onto it, giving the analysts more capability than they had while taking the load off the operational system. The product team stops fielding complaints about slowness it did not cause, and the analysts stop being asked to run their queries at night.
The AI project that turned out to be a data project
A team wants to build a prediction, a recommendation or a grounded assistant, and discovers the data it depends on is incomplete, inconsistent, or trapped in a system nobody can export from cleanly. We do the unglamorous work that makes the idea possible: consolidating and cleaning the relevant data, preserving the history the model needs, and building the pipelines that keep feeding it once it is live. Far cheaper to find this out at the start than after a model has been trained on data that could not support it.
How we approach Data Engineering
We work backwards from the decisions, not forwards from the sources. The first conversation is about the handful of questions the business genuinely needs answered, because that determines everything downstream: which sources are in scope, how fresh the data has to be, what grain the models need, and how much of the estate can be left alone for now. Teams that start from "let us bring all our data into one place" spend a year ingesting things nobody asks about and arrive at a large, expensive platform that still cannot answer the three questions that prompted the project.
Then we build for trust from the source down. Pipelines are observable and testable rather than a nightly job that either worked or did not; quality checks sit at the boundaries so bad data is caught rather than propagated; the warehouse is kept separate from production so analysis never competes with the application; and lineage is visible so a questioned number has an answer. We treat the business definitions as the most valuable artefact in the platform: what counts as an active customer, when revenue is recognised, which of the four dates on an order is the one people mean. Those get agreed, written down once in the transformation layer, and reused, because a metric redefined inside every dashboard is one that will disagree with itself in front of your board.
How the engagement runs
We start with the questions and the sources, in that order. Sessions with the people who actually use the numbers establish what decisions the platform must support, how fresh the data has to be for them, and where today’s answers come from, including the manual steps and private spreadsheets everybody knows about and nobody documents. In parallel we examine the sources: how each authenticates, whether it can be read incrementally, how it behaves under load, and whether it will warn anyone before it changes shape. Requirement and source constraints together decide the architecture, and a design made from only one of them falls apart on contact with the other.
Out of that comes a written design and a build order, including what is deliberately deferred and why. We are opinionated about starting narrow: the first delivery is a thin vertical slice, one genuinely useful question answered end to end from ingestion through modelling to something a person can look at, rather than six months of ingestion before anyone sees value. Then we widen one source or subject area at a time, with quality tests and lineage written as each model is built rather than retrofitted. We run against realistic volumes early, and watch cost from the first week, because the difference between a well-partitioned incremental model and a nightly full refresh is the difference between a predictable bill and an awkward conversation with finance. Handover is designed in rather than tacked on: everything lives in your repositories and cloud accounts from day one, and where you have your own data people we work alongside them so knowledge transfers as the platform is built.
How we architect a data platform
The first structural decision is separating analytical workloads from operational ones, and it is not negotiable in any platform of consequence. Operational databases are optimised for many small transactions against current state; analysis is the opposite, scanning large volumes of history in columnar fashion. Run both on the same infrastructure and each degrades the other, with the worst kind of failure mode: intermittent, load-dependent and felt by customers. So data moves out into a warehouse or lakehouse built for the second workload, and is read from there thereafter.
Within that we structure the platform in layers, currently labelled bronze, silver and gold, which is an old idea with a new name. Raw data lands first and is preserved untouched, because the raw layer is what lets you recompute when a transformation turns out to have been wrong, without going back to a source system and hoping it still holds the history. Above it sits a cleaned, conformed layer where types are standardised and entities resolved. Above that sit the modelled tables the business consumes, shaped around questions rather than around source-system structure. The discipline this enforces is that a mistake in business logic is a rebuild rather than a data-loss event.
The warehouse-versus-lakehouse question we answer on your situation, not on fashion. A managed warehouse such as Snowflake or BigQuery is the right default for most organisations: simpler to operate, with the performance work largely done for you and a lower total cost of ownership once you count the engineering time a self-managed alternative consumes. A lakehouse on object storage with Iceberg or Delta earns its place when you have genuinely large or semi-structured data, when several engines must read the same tables, or when open, portable storage is a strategic requirement rather than a preference. And for a surprising number of organisations the honest answer is smaller than either: a well-modelled Postgres instance will serve a business with tens of millions of rows for years.
Freshness drives the batch-versus-streaming decision, and the honest answer is usually batch. If people act on the data daily or hourly, a scheduled pipeline on that cadence is simpler, cheaper and far easier to reason about and recover. Streaming, with change data capture off the source database and a continuously running processing layer, is right when the business genuinely acts within seconds: fraud checks, live operational dashboards, real-time personalisation. It is harder to build and to keep running, and its cost is ongoing rather than one-off, so we reach for it when the requirement justifies it and talk you out of it when it does not. Orchestration ties the whole together, usually Airflow, Dagster or a cloud-native scheduler, with dependencies explicit, retries sensible, backfills a first-class operation, and every task idempotent so a re-run is always safe.
Protecting data at rest, in motion and in the warehouse
A data platform concentrates risk by design. You are deliberately gathering into one place information that was previously spread across systems with different access controls, and that concentration is exactly what makes the platform valuable and exactly what makes it worth protecting properly. So access control is designed rather than inherited: role-based access with permissions granted to roles rather than individuals, row-level security where different teams should see different slices of the same table, and column-level masking or tokenisation on fields most people have no business seeing in the clear. Pipeline credentials are scoped narrowly to the objects they read and write, held in a secrets manager, rotated, and kept out of code, configuration and logs, because a pipeline that logs a connection string has quietly published the keys to the systems it touches.
Personal data needs handling decisions made early, because a pipeline will otherwise spread it into every downstream system it feeds and every extract taken from those systems. We identify it at ingestion and then mask, tokenise, hash or simply do not carry fields the analytical use case does not genuinely need. Minimisation is the cheapest control there is: the field you never ingested cannot leak, cannot be over-retained and need not be found when someone exercises a right of erasure. For UK organisations that means engaging properly with UK GDPR rather than gesturing at it: a lawful basis for the analytical processing, retention implemented as actual deletion rather than written in a policy nobody enforces, a defensible answer to subject access and erasure requests across the warehouse and its downstream copies, and clarity about residency when a managed warehouse or connector would move data to another region. Around that, the usual production discipline: infrastructure as reviewable code, encryption in transit and at rest including the preserved raw layer, and access logs retained so an unusually large extract at an odd hour is visible rather than invisible.
Signs it’s time
- Reports take days to produce and, when they arrive, people still quietly check the numbers against their own spreadsheet before acting
- The same metric means different things in different teams, so meetings start by reconciling figures instead of deciding anything
- Analytics queries run against the production database and have started to slow the application your customers use
- A number was wrong in front of someone senior, and nobody could establish where it came from or how long it had been wrong
- You want AI, machine learning or personalisation features and have found the data they depend on is not in a state that can feed them
Trust is the deliverable
Everything here follows from one observation: a data platform is worth only what people are willing to act on, and belief is fragile. One wrong number in front of a senior audience does more damage to adoption than six months of good delivery repairs, because afterwards everyone quietly resumes keeping their own spreadsheet and the platform becomes an expensive record of numbers nobody uses. So we optimise for being right and being able to prove it. Tests run in the pipeline rather than in someone’s head, definitions are agreed with the people who own them and implemented once, and lineage is generated from the code so it cannot drift from reality. When a figure is questioned, the platform answers the question, which is what turns a reporting system into something the organisation relies on.
The second principle is that scope discipline is a technical skill. The instinct on a data project is to ingest everything, because leaving something out feels like a decision you might regret. The opposite holds: every source is a permanent obligation, a schema that will change without warning, a set of quality checks to maintain and a share of the compute bill, and a source nobody queries is pure liability. We would rather deliver a narrow platform that is completely trusted than a broad one that is patchily correct, because trust does not average out. People do not partially believe a dashboard. The same discipline makes us honest about the size of the answer: some organisations need three disputed definitions agreed and one reliable pipeline, some need their existing platform fixed rather than replaced, and some are being sold a lakehouse for a dataset that would fit comfortably in a well-indexed relational database.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Data Engineering?
A short call with a senior engineer, before you write a brief. If Data Engineering is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
Numbers you can defend
Data with quality checks and lineage you can point to, so when a figure is challenged there is a traceable answer rather than an argument, and people stop keeping private spreadsheets as insurance.
Answers in minutes
A warehouse modelled for the questions people actually ask, so analysis that used to mean a week of manual extraction and reconciliation becomes a query someone runs while the meeting is still on.
A foundation that holds
Clean, documented, retrievable data with the definitions agreed once, which is what every later ambition (self-serve analytics, forecasting, AI features) quietly depends on and rarely gets.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- A paid discovery and assessment first, on any engagement of size: a short, fixed-scope piece of work establishing what questions the platform must answer, what state your sources are actually in, and what should be built in what order, producing a written design and a costed plan. It is deliberately self-contained, so the output is yours to act on with your own team or anyone else, and nobody commits to a platform build before the shape of the problem is understood.
- Fixed-scope build for a defined platform: an agreed set of sources, modelled outputs and refresh cadence, quoted once discovery has established what the sources really do. The cost drivers are the number and awkwardness of those sources (a documented REST API is a very different proposition from an undocumented legacy database carrying thirty years of exceptions), the complexity of the business logic being encoded, how much history must be migrated and reconciled, and how strict the freshness requirement is.
- A monthly senior engagement where the platform keeps evolving: sources come and go, definitions change as the business does, new subject areas arrive, and someone needs to own reliability, quality and cost as a standing concern rather than a finished project.
- Third-party costs are billed to your own accounts, not marked up through us. Warehouse compute and storage, managed connector subscriptions, orchestration hosting and BI licences are contracted in your name and paid directly, which keeps the running economics visible and leaves nothing to unpick if you later take operation in-house or move it elsewhere.
Typical timeline
- 01
Discovery and design
Typically two to four weeks establishing the decisions the platform must support, auditing how the sources really behave, agreeing the core business definitions with the people who own them, and producing a written architecture and build order with the deferrals stated explicitly.
- 02
First vertical slice
One genuinely useful question answered end to end: ingestion, raw landing, modelling, tests and something a person can look at. This proves the architecture against real data and surfaces awkward source behaviour while it is still cheap to design around.
- 03
Widening the platform
Additional sources and subject areas added one at a time, with quality tests, documentation and lineage written as each model is built rather than retrofitted. Run against realistic volumes throughout, with cost monitored as the estate grows rather than discovered at quarter end.
- 04
Hardening, enablement and handover
Monitoring tuned to the failures that matter, recovery and backfill procedures tested rather than assumed, runbooks completed, and your own analysts and engineers brought up to speed so the platform is operable without the people who built it.
What working with us actually means
Senior engineers who have been paged at 3am
Data platforms are straightforward to demonstrate and hard to run for years. The difficult parts are late-arriving data, an upstream schema change nobody announced, a backfill that has to be safe, a silent partial load that made three weeks of reporting subtly wrong. The people building yours have dealt with those in production, so the unglamorous disciplines are present from the first pipeline rather than added after the first bad month.
We operate what we build
Because we run the platforms we design, operational reality shapes the design rather than being someone else’s problem. Monitoring, alerting, recovery procedures and cost controls go in from the start, because we are the ones who would otherwise be woken by their absence. Nobody who has to keep a system running ships one that is quietly untenable to operate.
You own all of it
The pipelines, models and infrastructure live in your repositories and your cloud accounts, defined as code your own engineers can read and change. There is no proprietary layer of ours in the middle that you must keep paying for to keep the platform working, and no rebuild required if you bring operation in-house. Handover is the design goal, not a negotiation at the end.
Honest about the size of the answer
We will tell you when a warehouse is premature, when your existing platform needs fixing rather than replacing, when streaming is complexity you do not need, and when a properly modelled relational database will serve you for years. That candour costs us work occasionally, and it is why the recommendations we do make are worth weighing.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Services in this practice
The specific services that make up this practice.
Related terms
Weighing the options
The decisions people are usually making at the same time as this one.
Common questions
What is the difference between data engineering and the more specific services like ETL or data warehouse development?
Data engineering is the whole discipline; the others are parts of it. ETL development is the movement: building and orchestrating the pipelines. Data warehouse development is the destination: the analytical store and how it is modelled. Big data covers the techniques you need once volume or velocity breaks ordinary tooling. Data management is the ongoing governance and stewardship discipline. Business intelligence is the consumption layer people look at. If you already know which piece you need, go straight to it. If what you have is scattered data, reporting nobody trusts and no clear view of what to build first, start here, because the most valuable early decision is which of those parts you need and in what order.
Do we really need a data warehouse, or can we keep reporting from our production database?
You can report from production for a surprisingly long time, and plenty of organisations should. It stops working at one of three points: analytical queries start affecting application performance for real users, the questions need history the operational database does not keep because it holds current state, or reporting needs data from several systems that no single database contains. If none of those apply yet, a warehouse may be premature and we will say so. If one does, the separation is worth doing properly, and doing it before the performance problem becomes customer-visible is considerably less stressful than after.
Our numbers already disagree between teams. Does a platform fix that?
Only partly, and this is where data projects most often disappoint. Disagreeing numbers are usually not a technical problem but a definitional one: finance counts revenue on invoice date, sales counts it on close date, and both are correct within their own frame. A platform cannot resolve that, it can only implement whatever definition you agree. So part of our work is facilitating that agreement with the people who own each metric, then implementing it once in the transformation layer so it is reused rather than reimplemented differently in each dashboard. The technology enforces the agreement; it cannot substitute for it. If your organisation will not have the definitional conversation, no platform will make the numbers agree.
We want to build AI features. Should we do that first or fix the data first?
In most cases the data comes first, and since that sounds self-serving, here is the reasoning. AI features that depend on your own data inherit every weakness in it. If entities are not resolved, the model sees the same customer as four. If history has been overwritten rather than preserved, the model trains on figures as they were later restated rather than as they were known at the time, which quietly leaks the future into the training set. If the pipeline computing a feature in training differs from the one computing it in production, the model performs worse in the real world than in testing and nobody can explain why. None of that is fixed by a better model, which is why a stalled AI project so often turns out to have been a data project the whole time.
Thinking about Data Engineering?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Data Engineering is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.