/ CASE STUDY
Company-wide modern data platform for an engineering enterprise
Industry
Engineering
Size
1500+
Solution
Platform
Overview
This engineering firm had data in many systems and strong operational processes, but no unified way to access, model, and share it. We implemented a modern platform on Azure Databricks that ingests from core apps, models business-ready datasets with dbt, and exposes a governed catalog used across teams. The result is a single place to discover, trust, and use company data - for dashboards, data products sent to other systems, and entity mapping that underpins a true enterprise source of truth.
Days
to insights, not weeks
Single
source of truth
The challenge
Data lived in silos: HR, finance, project and custom tools. Each team exported CSVs and built their own logic. Definitions drifted, integrations were brittle, and analytics moved slowly. Leadership wanted one platform that fit existing processes, didn’t disrupt operations, and could scale to new sources and use cases.
What we built
Azure-native foundation
Landing zones, storage, and security primitives set up to enterprise standards (RBAC, private networking, key management).
EL pipelines to bring in data on reliable schedules.
Databricks for processing and interoperability.
Medallion architecture (bronze → silver → gold) for raw, cleansed, and business-ready data.
Delta Lake for ACID reliability, time travel, and scalable incremental upserts.
dbt for modeling, data tests and governance
Version-controlled transformations with tests (unique, not null, referential integrity).
Documented models and metrics for projects, parts, customers, suppliers, assets, and work orders.
Central data catalog as the hub
A live, governed catalog that lists assets, owners, lineage, and access policies.
Data products for analytics and integrations
Gold datasets powering dashboards or being used to send back information to other tools.
Entity resolution to map company concepts (customers, projects, organisation) into a single, consistent layer.
CI/CD for automated deployment and automated quality control.
Why this works
Fits existing processes while giving one place to find and trust data.
Scales cleanly: add a new source or use case by following the same ingestion → model → catalog pattern.
The catalog is the front door: discoverability, ownership, and access are built-in, not afterthoughts.
What changed
Teams pull consistent, documented datasets instead of exporting and merging spreadsheets.
Dashboards and integrations use governed models, so definitions match across finance, operations, and sales.
New data products ship faster because quality checks and contracts are standardized.
Results
A single, governed source of truth adopted across departments.
Faster delivery of analytics and system-to-system data feeds.
Fewer reconciliation meetings; metrics line up between finance and operations.
