Analytics Engineering
dbt models with tests, docs and a semantic layer, so revenue means the same thing in finance, sales and the warehouse.
Talk this throughData engineering & ops
The layer everything else quietly depends on
Ingestion, transformation and observability built so that nobody has to debug a number during a board meeting. Unglamorous, and the reason the rest works.
MWRKJT
Delivery leads online — talk to a human, not a bot50+Sources unified
DailyAutomated ingestions
TestedPyTest & Great Expectations
Platform layersIngest → serve
100% delivery success — delivered worldwide across seven regions
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
What you get
Pipelines are a product, not a script
Ad hoc extracts and cron jobs work until the person who wrote them leaves. We build pipelines with contracts, tests, lineage and owners — the same standards you would apply to production software.
- Real-time and batch data ingestion from any source, into one place
- Modelled, tested and documented tables in version control
- Data pipeline orchestration — automated, monitored and reliable
- Freshness, volume and distribution checks with routed alerting
- Column-level lineage from raw file to the figure on the slide
- Data governance and security — access controls, policies and encryption
End-to-end flowSources → decisions
Capabilities
What the pod builds
Delivered as one team with a single backlog you can see.
Managed Data Engineering
An embedded pod that owns your pipelines end to end — build, run, on-call and improvement — on a fixed monthly footprint.
Talk this throughData Integration
Every source connected: ERP, CRM, product events, files, APIs and the spreadsheet somebody keeps on their desktop.
Talk this throughData Observability
Freshness, volume, schema and distribution monitoring with alerts routed to an owner and a runbook, not a group chat.
Talk this throughETL & ELT Pipelines
Batch, streaming and change data capture, sized honestly against your volumes rather than a vendor's reference architecture.
Talk this throughBy the numbers
Data engineering in numbers
Grounded in real MOD engineering work across AWS, Azure and Google Cloud data platforms.
0+Data sources unified into one source of truth
0%Manual workload removed on a marketing transformation
0%Pipelines tested before they ship
0/7Automated, monitored data delivery
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
01
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
02
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
03
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
04
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
05
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
06
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Recent work
Marketing · EMEA
Marketing and CRM data unified across 20+ EMEA countries
A digital-transformation programme that consolidated marketing and CRM data across the region on Azure and Power BI, with a custom lead-management automation built on Power & Logic Apps.
80%Manual workload cut
20+EMEA countries
FMCG · Nestlé
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
DailyAutomated ingestions
MedallionData architecture
Data strategy · Germany
One source of truth from 50+ scattered sources
Data strategy and cloud architecture on Google Cloud, ingesting 50+ sources through Stitch, modelled in dbt and surfaced to the CEO and Head of Product in Looker Studio.
50+Sources unified
ExecLive dashboards
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionUsually not. Most of the value comes from modelling and testing what you already have. We only recommend a platform move when the current one is actively costing you — and then we phase it.
That is the preferred setup. Our pods embed in your sprints and your repositories. Pairing is written into the plan, not offered as an extra.
Alerts route to whoever is on call under the rota we agree. During an engagement that is us. After handover it is your team, with runbooks we wrote together and a support retainer if you want one.
Fixed-scope for assessments and discrete builds; a monthly pod rate for managed engineering. No per-seat licensing, no markup on cloud spend.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.