Sound familiar?
Data challenges that stall growing businesses
The data fragmentation, quality and accessibility problems that hold back teams who are drowning in data but starving for insight.
Data scattered across disconnected tools
Unified data pipelines pull from every source — CRM, product, payments, marketing — into a single warehouse your whole team can query.
No single source of truth
dbt data models create a consistent, documented layer of business metrics — so every team works from the same definitions and numbers.
Manual reports eating team time
Automated dashboards and scheduled reports replace manual spreadsheet work — freeing analysts for actual analysis rather than data wrangling.
Data not structured for ML or AI
ML-ready feature stores and clean, labelled datasets that your data science team can use immediately — no data prep backlog blocking model development.
What we offer
Data engineering across the full analytics stack
Six capability areas from raw ingestion to production dashboards and ML-ready infrastructure.
Data pipeline & ETL engineering
Reliable, monitored pipelines from every data source — SaaS tools, databases, event streams — into your warehouse. Custom connectors where Fivetran or Airbyte don't cover it.
- Incremental & full-refresh strategies
- Pipeline monitoring & alerting
Data warehouse & lakehouse architecture
Design and implementation of Snowflake, BigQuery, Redshift or DuckDB warehouses — with cost-optimised partitioning, access controls and documentation standards.
- Schema design & data modelling
- Cost governance & query optimisation
Analytics engineering (dbt)
Business-ready data models, documented metrics layer and tested transformations that turn raw warehouse data into consistent, trusted analytical datasets.
- dbt project setup & model lineage
- Metrics store & semantic layer
BI dashboards & self-serve reporting
Production-grade dashboards in Looker, Metabase, Power BI or Tableau — built on your clean data models so reports are always accurate and self-serve for the whole team.
- Executive & operational dashboards
- Analyst training & self-serve enablement
ML-ready data infrastructure
Feature stores, training dataset pipelines and the data reliability guarantees your ML team needs — so data prep doesn't become the bottleneck for AI product development.
- Feature engineering & store setup
- Training / serving data split patterns
Data quality, governance & cataloguing
Automated data quality tests, lineage documentation and a data catalogue so your team trusts the data and your compliance posture is audit-ready.
- dbt tests & data observability
- Data catalogue & ownership framework
How we work
A data platform delivery approach built for startups
From audit to operationalised platform — predictable phases with clear outputs at each stage.
Audit
Map your current data landscape — sources, tools, quality and gaps — before recommending anything.
Model
Design the target data architecture — warehouse choice, data model, ingestion strategy and reporting layer — agreed before build.
Build
Pipelines, warehouse, dbt transformations and dashboards — iterative delivery with working data at every sprint.
Operationalise
Monitoring, alerting, documentation and team enablement so your platform runs reliably without us in the loop.
What you receive
A fully owned, documented data platform
No black boxes. Every pipeline, model and dashboard is documented, tested and yours to own and extend.
Data audit
- Data landscape map & source inventory
- Quality assessment & gap analysis
- Tool & stack recommendations
Data platform
- Warehouse setup & ingestion pipelines
- dbt project with documented models
- Production dashboards & alerting
Handover & enablement
- Runbooks & pipeline documentation
- Data catalogue & ownership matrix
- Analyst training & self-serve guides
Technology
Modern data stack, best-fit for your scale
We work across the tools your team already uses — and will recommend the right additions for your stage, not the most expensive ones.
Warehouses
Snowflake
BigQuery
Redshift
DuckDB
Databricks
Ingestion & transformation
Airbyte
dbt Core / Cloud
Stitch
BI & visualisation
Looker / LookML
Metabase
Power BI
Tableau
Superset
Orchestration & quality
Airflow / MWAA
Prefect
Great Expectations
Monte Carlo
Engagement models
Clear pricing to fit your data maturity
Whether you need a quick data audit or a full platform build, we scope engagements clearly upfront with no surprise billing.
Fixed price
Data audit
Data landscape map, source inventory, quality assessment and tool recommendations — delivered within one week.
Time-boxed
Data platform build
6–10 weeks to a production-ready warehouse, pipelines, dbt models and dashboards. Fixed scope, fixed price.
Monthly retainer
Analytics engineering
Ongoing analytics engineering support — new models, dashboard additions, pipeline maintenance — for teams without an in-house data engineer.
Ready to talk through your project? No commitment required.
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Let's talk
Book a free 30-minute discovery call
Tell us about your data landscape, the decisions you're struggling to make, and what reliable data would change for your business.
- No obligation — just a conversation
- Scoping proposal within 48 hours
- Can start within 2 weeks of sign-off