A single source of truth
Consolidate data scattered across applications, spreadsheets and exports into a governed warehouse or lakehouse that the whole business reports from.
Learn: do teams stop arguing about whose numbers are right?
Services / Data & Cloud
Data engineering.Numbers your business can act on.
We design and build the data platforms, pipelines and governance that turn scattered sources into reliable, documented data: for reporting, for operations and for AI features. Your team owns the result.
Tested pipelines. Measured data quality. Architecture sized to your reality.
The business case
Decisions are only as good as the data behind them.
When every team reports different numbers, meetings become arguments about whose spreadsheet is right. Reliable data is an investment in decision speed: teams that trust their numbers spend their time acting on them.
When this service fits
Reporting depends on manual assembly, data lives in disconnected systems, pipelines break without anyone noticing, or AI ambitions are blocked by data nobody can find or trust. Access to source systems and the people who know them is essential.
When something else fits better
If you need a single dashboard from one source, a lighter analytics setup may serve better than a platform build. If the goal is purely an AI feature, our AI engineering services can start there and pull in data work as needed.
Where this applies
Different data problems. One engineering discipline.
These are example situations, not pre-packaged promises or claims about past results.
Reporting that runs itself
Replace manual report assembly with pipelines that refresh on schedule, so analysts spend time on questions instead of copy-paste.
Learn: how many hours of manual reporting disappear each week?
Data for AI and ML features
Build the clean, documented, permissioned datasets that AI features depend on, since a model trained on unreliable data fails expensively.
Learn: can AI teams find and trust the data they need without asking around?
Real-time operational data
Stream events from products, devices or transactions into systems that act on them in seconds, where the business case justifies the complexity.
Learn: do decisions that needed fresh data actually get it in time?
Migration off fragile pipelines
Rebuild undocumented, breakage-prone data jobs into tested, monitored pipelines with clear ownership.
Learn: do pipeline failures stop reaching the people who read the reports?
Compliance-ready data handling
Lineage, retention, access control and audit trails for data under privacy or financial regulation, built into the platform rather than bolted on.
Learn: can you answer where a piece of data came from and who touched it?
How the platform works
Every dataset passes the same checks.
Data flows from source systems through tested, monitored pipelines into a governed platform. Quality checks run at each stage; failures route back with evidence before bad data reaches a decision.
What we always include
Tested transformations, data quality checks, lineage documentation and monitoring. These are what make data trustworthy enough to act on.
What we keep out
Streaming where batch is cheaper, tools chosen for fashion rather than fit, and platforms sized for volumes you will not reach for years.
Architecture & platform design
Designed around your questions, not a vendor diagram.
The right architecture depends on your volumes, your team's skills and the decisions the data supports. We design for what you need now with room to grow, not for a conference talk.
Warehouse, lakehouse or both
An honest assessment of whether you need a warehouse, a lakehouse, or simply a well-run database with good reporting, sized to your actual volumes.
Modelling for the business
Data models built around the entities and questions your business cares about, documented so a new analyst can navigate them.
Batch and streaming, chosen deliberately
Streaming only where freshness has a measurable business value; batch everywhere it is cheaper and simpler, which is most places.
Cost-aware design
Storage and compute choices made with the bill in mind, with budgets and alerts so costs stay visible as data grows.
Pipelines & integration
Data that arrives on time, correct and explained.
Pipelines are software: they deserve tests, monitoring and ownership like any production system. We build them that way.
Ingestion from your systems
Connectors and change-data-capture from applications, databases, SaaS tools and files, with schemas handled explicitly rather than hoped for.
Tested transformations
Transformation logic versioned in code, tested against known inputs and reviewed like application code, not hidden in a tool's UI.
Orchestration & dependencies
Scheduled and event-driven workflows with explicit dependencies, retries and backfills, so a failure is isolated instead of cascading silently.
Data quality checks
Automated validation at each stage: freshness, volume, schema and business-rule checks that fail loudly before bad data reaches a dashboard.
Governance, quality & trust
Numbers people can rely on, with evidence.
Trust in data is earned through lineage, quality measurement and clear ownership. Without it, every report gets re-checked by hand.
Lineage & documentation
Where each dataset comes from, how it is transformed and who owns it, documented and queryable rather than tribal knowledge.
Data quality measurement
Quality scores and incident history per dataset, so trust is measured and improving rather than assumed.
Access control & privacy
Role-based access, masking of sensitive fields and retention rules enforced in the platform, aligned with your privacy obligations.
Semantic consistency
Shared metric definitions so revenue, active users and churn mean the same thing in every report and every tool.
Analytics & AI enablement
The foundation your dashboards and AI features stand on.
Data engineering earns its keep downstream: faster reporting, self-serve analytics and AI features built on data that holds up.
Self-serve analytics
Curated, documented datasets and semantic layers so business users answer their own questions without queueing for an engineer.
ML & AI data preparation
Feature pipelines, training datasets and retrieval corpora with the freshness, labelling and permissions AI features require.
Reverse flows where useful
Modelled data pushed back into operational tools, like scores into the CRM, where it changes what people do rather than decorating a dashboard.
Adoption & literacy
Pairing with analysts and stakeholders so the platform is actually used, with training materials that survive our departure.
Tools & frameworks
Chosen for your stack, maintained by your team.
We work with the tools that fit your volumes, team skills and budget. These are options, not a requirement to use everything on the list.
Warehouses & lakehouses
SnowflakeBigQueryDatabricksPostgreSQL
Storage and compute matched to your volumes, team and budget.
Ingestion & integration
FivetranAirbyteDebezium / CDCCustom connectors
Reliable movement from source systems with explicit schema handling.
Transformation
dbtSQLMeshSpark
Versioned, tested transformation logic in code with documentation.
Orchestration
AirflowDagsterPrefect
Dependencies, retries, backfills and schedules managed explicitly.
Streaming
KafkaRedpandaKinesis
Event streaming where freshness has proven business value.
Data quality & observability
Great Expectationsdbt testsMonte CarloOpenLineage
Freshness, schema and business-rule checks with lineage tracking.
BI & semantic layer
LookerMetabasePower BICube
Dashboards and self-serve analytics on governed definitions.
Infrastructure & languages
PythonSQLTerraformDocker / Kubernetes
Infrastructure as code and pipelines in maintainable languages.
Tool selection depends on your volumes, team skills, cloud commitments and licensing. We prefer fewer, well-maintained tools over a broad stack nobody owns.
Delivery approach
From assessment to a platform your team runs.
You work with engineers inside the delivery process. Reviews, demonstrations and scope decisions stay connected to the decisions your data should support.
- 01
Assess
Map your sources, current pipelines, reporting pain and the decisions data should support, then identify the highest-value first move.
Output: Data landscape assessment and prioritised roadmap.
- 02
Design the foundation
Agree the architecture, modelling approach, quality standards and access model before building.
Output: Architecture decision record and platform design.
- 03
Build the core
Implement the platform, the first priority pipelines and quality checks, delivering one visible reporting win early.
Output: Running platform with priority datasets in production.
- 04
Expand & govern
Broaden source coverage, add lineage, quality measurement and self-serve access, and retire manual reporting.
Output: Governed datasets, documented lineage and measured quality.
- 05
Hand over & enable
Transfer ownership to your team with documentation, pairing and operational runbooks.
Output: Runbooks, ownership model and a team that runs its own platform.
What your team brings
A decision-maker, access to source systems, the people who know where the data lives, and the analysts or stakeholders who will use the result. For regulated data, bring compliance stakeholders into scoping early.
Decisions without surprises
We show running pipelines early, keep quality scores and costs visible and document tradeoffs. Architecture decisions are recorded with their rationale, so future work builds on reasoning rather than guesswork.
Handover & ownership
Your platform. Your pipelines. Knowledge that stays.
Everything lives in your repositories and cloud accounts from day one. Handover includes the architecture rationale and the operational practices, so the platform stays healthy after we leave.
A platform your team owns
Pipelines, models and infrastructure live in your repositories and cloud accounts, maintained by your engineers.
Documented architecture
Decisions, tradeoffs and data models written down, so future work follows a rationale rather than archaeology.
Measured data quality
Freshness, accuracy and incident trends visible per dataset, so trust in the numbers is evidence-based.
Governance that scales
Access control, lineage and retention built into the platform, ready for growth and regulatory scrutiny.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing data engineering support, or hand over fully to your own team.
Related engineering services
Choose the support your next stage needs.
Data work often connects to AI features, new products or dedicated engineering capacity.
Questions before starting
The practical details.
We are not a big company. Do we need a data platform?
Maybe not a large one. Many businesses need a well-modelled warehouse, a handful of reliable pipelines and a BI tool, which is a few weeks of work, not a programme. We size the recommendation to your actual questions and volumes, and we will tell you if a simpler setup serves you better.
How long until we see value?
The first priority pipeline and a visible reporting improvement typically land within the initial two-week sprint. A broader platform builds incrementally over months, sequenced so each phase delivers something usable rather than a big reveal at the end.
Our current pipelines break constantly. Can you stabilise them?
Yes, and it is a common starting point. We triage what exists: fix what is fixable, add monitoring and quality checks, and rebuild the fragile parts with tests and clear ownership. The goal is pipelines that fail loudly and rarely, instead of silently and often.
Can you prepare our data for AI features?
Yes. AI features need clean, documented, permissioned data with known freshness. We build the training datasets, feature pipelines and retrieval corpora that AI work depends on, and we are direct about whether your data is ready for the AI you have in mind.
Which warehouse or platform do you recommend?
It depends on your volumes, team skills and existing cloud commitments. BigQuery, Snowflake and Databricks each fit different situations, and sometimes PostgreSQL is genuinely enough. We recommend after the assessment, not before it.
How do you handle sensitive data and privacy?
Access control, masking and retention are designed into the platform from the start, aligned with your privacy obligations. Sensitive fields are identified during assessment, and access follows least-privilege principles with an audit trail.
What does this cost to run afterwards?
We design with the running cost in mind and set up budgets and alerts so the bill stays visible. Costs depend on volumes and tooling choices; part of the architecture work is giving you a realistic estimate before you commit.
What does handover look like?
Your team owns everything: the platform, the pipelines, the documentation and the operational practices. We pair with your engineers throughout so knowledge transfers with the code, and we can stay available for review or further phases if useful.
Start with the question
Tell us which decision your data cannot support today.
We will build the foundation that changes that.
Share where the numbers fail you and how reporting works today. We will agree a practical first sprint.