Data Engineering & Platforms
The build. Pipelines, warehouses and lakehouses that hold up under load, with tests and alerting from day one.
Data engineering is the practice of designing and operating the pipelines, warehouses and lakehouses that move, transform and serve trustworthy data for analytics, products and AI.
The problem
Most AI and analytics failures are data failures in disguise. Spreadsheets, ad-hoc scripts and untested jobs work until volume, schema drift or a new product surface exposes them — usually in front of a customer or a board pack.
Building another dashboard on sand multiplies the confusion. The durable fix is upstream: contracts, tests and alerting on the paths that feed every chart, model and operational workflow.
Who this is for
- Scale-ups that have outgrown spreadsheet and script-based data work.
- Teams adopting a warehouse or lakehouse (Snowflake, BigQuery, Databricks and similar) who need production-grade ingestion and models.
- Product and AI teams blocked by unreliable, undocumented upstream data.
Outcome
A production pipeline or platform with tests, monitoring and documentation from day one.
What the engagement involves
- Week 1–2
Source map and target design
Inventory critical sources, agree entity definitions, and design the warehouse or lakehouse layout your analysts and products will actually use.
- Week 3–6
Build pipelines with tests
Implement batch and streaming ingestion as needed, transformations (including dbt where it fits), and data quality checks that fail loudly in CI or alerting.
- Week 7–8
Handover and harden
Monitoring, runbooks and documentation in your repos so your team can operate and extend the platform without us.
What you receive
- A production warehouse or lakehouse design documented with clear ownership.
- Ingestion and transformation pipelines in your cloud and source control.
- A starter data quality suite on the keys and freshness SLAs that matter.
- Alerting and operational notes your engineers can own.
Core deliverables
- Lakehouse & warehouse design
- Batch & streaming ingestion
- Data quality testing
What is a lakehouse, and do I need one?
A lakehouse is a data architecture that combines the flexible storage of a data lake with warehouse-style reliability — tables, governance and SQL performance — usually on open table formats.
You need one when you have diverse raw data and serious analytical or AI workloads on the same foundation. Many teams start with a well-run warehouse and grow into lakehouse patterns when the use cases demand it. We help you choose based on load, team skill and cost — not vendor fashion.
Do I need a data platform or just a warehouse?
A warehouse stores and serves modelled data. A data platform also includes ingestion, quality, orchestration, access control and the operational practices around them.
If analysts already fight broken feeds and nobody owns freshness, a warehouse licence alone will not fix it. We build the platform pieces that make the warehouse trustworthy.
Why ship data quality tests before more dashboards?
Dashboards multiply trust problems when upstream numbers disagree. Tests on freshness, volume, uniqueness and referential integrity catch breaks before they become Slack archaeology.
A small suite that fails loudly pays for itself faster than three more charts on sand.
Which tools do you work with?
We work in your cloud and your repos with the stack you already favour where it is sound — common warehouse and lakehouse engines, orchestration and transformation tools including dbt, and standard observability patterns. We do not lock you into a proprietary Inoviq platform.
Not sure this is the right fit?
A 30-minute discovery call is enough for us to tell you honestly whether this is what you need.
Book a discovery call