Insight Analytics
Service

Data Engineering

The platform underneath the dashboards — designed so adding the next source takes days, not another project.

Companies usually arrive at data engineering after the third or fourth point solution. Each report was built on its own connection, each team solved its own problem, and now there's no shared foundation to build on.

We design and build that foundation: a medallion-style lakehouse or warehouse with clearly separated raw, cleaned, and business layers; orchestration that manages dependencies rather than schedules-by-hope; and observability so problems surface as alerts, not as complaints.

Scope is deliberately staged. The first phase carries two or three high-value domains end to end, proving the pattern, and everything after that is repetition rather than invention.

Problems this solves

What we usually walk into

  • Every new report starts with a new bespoke data connection.
  • There's no environment separation — development happens in production.
  • Costs are climbing and nobody can attribute spend to a workload.
  • Data science and BI work from different, disagreeing copies of the data.

Technologies

What we build with

DatabricksMicrosoft FabricSnowflakedbtKafka / Event HubsTerraform

Industries that benefit

  • Technology
  • Healthcare systems
  • Financial services
  • Logistics
  • Manufacturing

Implementation process

How the engagement runs

  1. 01

    Architecture design

    Target platform, layer model, environments, and access design agreed and diagrammed before the build.

  2. 02

    Foundation build

    Infrastructure as code, storage layout, orchestration framework, and CI/CD pipelines.

  3. 03

    First domains

    Two or three business domains carried raw-to-serving to prove and refine the pattern.

  4. 04

    Observability and cost control

    Lineage, quality metrics, alerting, and workload cost attribution.

  5. 05

    Enablement

    Standards, templates, and training so your team adds the next domain without us.

Sample screens

What the finished work looks like

Representative layouts using demonstration data — client work is never shown without written permission.

Platform overview

Domains

7

Jobs

126

SLA

99.9%

Platform overview

Layer health, job status, and data freshness across domains.

Workload cost

Monthly

$4.8K

Per TB

$18

Idle

4%

Workload cost

Compute spend attributed by team and pipeline.

Layer composition

Bronze

42TB

Silver

11TB

Gold

0.9TB

Layer composition

Volume distribution across bronze, silver, and gold.

Illustrative example

How a data engineering engagement typically plays out

Anonymised scenario · not a verified client record

Healthcare services group, 3 acquired businesses

Challenge

Three acquisitions meant three EHR-adjacent systems, three finance stacks, and no way to report on the combined group without a two-week manual consolidation.

Solution

A Fabric lakehouse with a bronze-silver-gold model, conformed patient and encounter dimensions across all three sources, orchestration with dependency-aware scheduling, and freshness monitoring per domain.

Result

Group-level reporting became a daily refresh instead of a two-week project, and the fourth acquisition was onboarded to the platform in nine days.

Illustrative figures

2 wks → daily

Group consolidation

9 days

To onboard a new entity

99.9%

Pipeline SLA

This is a composite illustration of the scope, approach, and range of results this service is designed to deliver. It does not describe a specific named client, and the figures are demonstration values rather than audited outcomes. We're happy to talk through real references under NDA on a call.

Deliverables

What you receive

  • Architecture decision record and diagrams
  • Infrastructure-as-code repository
  • Medallion layer models for the first domains
  • Orchestration framework with dependency management
  • Observability dashboards and cost reporting

FAQs

Questions we get asked

Databricks, Fabric, or Snowflake?
It depends on your team's skills, existing licensing, and whether data science workloads matter. We make the recommendation in week one, with reasoning.
Do we need streaming?
Rarely at first. Most 'real-time' requirements are satisfied by hourly loads, and we'll challenge the requirement before building for it.
How do you control cloud spend?
Right-sized compute, auto-suspend, workload tagging, and a cost dashboard from the first week rather than after the first surprising invoice.
Can our existing team run this?
That's the goal. Standards and templates are part of the delivery, and we run enablement sessions throughout.

Related services

Often scoped alongside this

ETL Development

Reliable ingestion pipelines from any source to any target.

View etl development

Cloud Analytics

Modern lakehouse and warehouse architectures on Azure and AWS.

View cloud analytics

Data Warehousing

Dimensional models on Snowflake, Fabric, SQL Server, and Azure.

View data warehousing

Talk through your Data Engineering project

A 30-minute call is usually enough to tell you whether this is a two-week fix or a two-month build — and roughly what it costs.

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