Data Engineering & Analytics

Most companies have the data they need. It sits in a dozen systems, each team has its own export, and the monthly numbers only agree after someone spends two days reconciling them. Dashboards get built on top of that, nobody trusts them, and the spreadsheets come back. The fix is rarely a new tool. It is a small number of well-tested pipelines, one agreed definition for each measure, and reports built on top of both.

One set of numbers

Revenue, margin, churn, and headcount are defined once, written down, and used by every report.

Pipelines that tell you when they break

Each load is tested for freshness and quality, and the right person is told before a report goes wrong.

Reports people open

We design dashboards with the people who use them, around the decisions they make each week.

Data pipelines

Reliable loads from your ERP, CRM, finance, and product systems, with retries, alerts, and a record of every run.

Warehouse and lakehouse design

A clear structure of raw, cleaned, and reporting layers on Snowflake, BigQuery, Databricks, or Fabric.

Metrics and data modelling

Shared definitions for your key measures, built as tested code so the same question always gives the same answer.

Data quality and testing

Automatic checks for missing, late, duplicate, or odd-looking data, with an owner named for each one.

Dashboards and self-service

Reports in Power BI, Tableau, or Looker, built with your users, plus a governed model people can explore themselves.

Data catalogue and lineage

A searchable list of what each table means, who owns it, and which reports depend on it.

• Where should the data live?

  How we decide

  A comparison of warehouse, lakehouse, and hybrid options against your data volumes, skills, and existing licences.

  What you get

  A platform choice with expected running costs for the first two years.

• Batch or real time?

  How we decide

  We ask what decision each report supports and how fresh it needs to be, then price the difference.

  What you get

  Real time only where it earns its cost, and nightly or hourly loads everywhere else.

• Whose definition of a metric wins?

  How we decide

  A working session with finance and the business lead to agree each key measure, with examples from real figures.

  What you get

  A metrics dictionary, built into the model and signed off by a named owner.

• How do we keep it trustworthy?

  How we decide

  Tests on every load and a short list of checks for each business-critical table, built with the source owner.

  What you get

  Alerts to the right person, and a data quality page anyone can read.

• Who can see what?

  How we decide

  Access rules designed with your security team, covering roles, sensitive columns, and regional rules.

  What you get

  Role-based access that is reviewed and recorded, so audits are simple.

1. Phase 1

   2 weeks

   Assess

   • Interview the people who build and read today’s reports
   • Map the source systems and trace the five most-used reports back to their data
   • Agree which measures to fix first and how success will be judged

   You receive

   Current-state map, priority list of measures, and target design.

2. Phase 2

   4 to 6 weeks

   Build the foundation

   • Set up the platform and connect the first sources
   • Build and test the models for the priority measures
   • Review the numbers against your current reports with the business lead

   You receive

   Working pipelines, tested models, and a reconciliation against today’s figures.

3. Phase 3

   2 to 3 weeks

   Report

   • Design the first dashboards with the people who will use them
   • Train a small group and collect their feedback
   • Retire the manual reports that are now replaced

   You receive

   Live dashboards, user guides, and a list of retired reports.

4. Phase 4

   Ongoing

   Extend and run

   • Add sources and measures in a steady order of priority
   • Monitor quality, speed, and cost
   • Hand more of the work to your own team each quarter

   You receive

   Runbook, monitoring, and a quarterly plan for the next sources.

How we handle your data

Least-privilege access

We get read access to the sources we need and nothing more, and it is removed when the work ends.

Sensitive data protected

Personal and confidential fields are masked or restricted by role from the first load.

Your tenant, your control

The platform runs in workspaces and accounts you own, so the data stays where your policies say it should.

Everything documented

Each pipeline, model, and metric comes with plain-language notes, so your team can maintain it without us.

Changes reviewed and tested

Nothing reaches production without code review and passing tests, with a record of who approved it.

Tools we work with

Platform

Microsoft Fabric, Databricks, Snowflake, Google BigQuery

Ingestion

Fabric Data Factory, Azure Data Factory, Fivetran, Airbyte, custom connectors

Transformation

Spark, dbt, SQL, Python

Orchestration

Databricks Workflows, Fabric pipelines, Airflow, Dagster, Prefect

BI and reporting

Power BI, Tableau, Looker, Metabase

Quality and catalogue

dbt tests, Great Expectations, DataHub, Atlan, Collibra

We build on the platforms you already pay for, wherever that makes sense.