Data Solutions
Data Engineering
Pipelines and platforms that move and hold data reliably
Data that arrives late, twice, or not at all quietly breaks every report built on it. We build the pipelines and platforms that bring data from the systems you run into one well-organized home, in nightly batches or as events happen, and that recover on their own and say clearly when something goes wrong.
- Source ingestion
- Warehouse and lakehouse
- ELT transformations
- Real-time streaming
- Orchestration
- Monitoring and alerting
Why it matters
A pipeline you cannot explain is a liability. Every run should be traceable, repeatable and loud when it fails, and every layer rebuildable from the one before it.
What this includes
Six parts of the work. Which of them you need depends on where you are starting from, and we agree that before anything is built.
- 01
Source ingestion
Connectors for databases, APIs, files and SaaS tools, landing data on a known schedule in a known shape.
- 02
Warehouse and lakehouse
One layered home for analytical data, with raw, cleaned and business-ready data kept apart.
- 03
ELT transformations
Cleaning and modeling inside the warehouse with version-controlled, tested SQL.
- 04
Real-time streaming
Event and change-data pipelines for the cases where minutes genuinely matter.
- 05
Orchestration
Dependencies, retries and backfills handled by a scheduler instead of cron jobs and hope.
- 06
Monitoring and alerting
A record of every run, and an alert that names the failed step when one fails.
Not sure which of these you need?
Tell us where things stand for you today and we will suggest where to start.
How we deliver it
Four stages, agreed with you before the work starts, with a working result at the end of each rather than one reveal at the end.
Inventory the sources
We list each source, its volume, how often it changes and how to access it safely.
Design the platform
We agree the platform, layers, schedules and ownership before building.
Build and test
Pipelines are built in code, tested with real data and reviewed like any other software.
Run and hand over
We monitor the first cycles in production and hand over runbooks and access to your team.
The stack
Technologies we work with
The tools we reach for most often in this work. We fit in with what you already run rather than asking you to change it.
3 groups
- 01 / 033
Orchestration
- Apache Airflow
- AWS Step Functions
- Azure Data Factory
- 02 / 035
Platforms
- Snowflake
- Amazon Redshift
- Azure Synapse
- Databricks
- Amazon S3
- 03 / 035
Streaming and build
- Apache Kafka
- Amazon Kinesis
- Python
- dbt
- Docker
What you receive
Yours to keep when the engagement ends, whether we carry on working together or not.
Running pipelines
Scheduled, monitored pipelines for each agreed source, in your own cloud account.
A documented data platform
Your warehouse or lakehouse, with every table's source, grain and owner recorded.
Runbooks
What to do when each alert fires, written for the people who will receive it.
A data flow diagram
Where every dataset comes from and where it goes, kept current.
Common questions
The ones that come up in almost every first conversation about this work.
Most reporting needs are met by scheduled batch loads, which are simpler and cheaper to run. We recommend streaming only where a decision genuinely needs data within seconds or minutes.
A warehouse suits structured reporting well. A lakehouse adds flexibility for large, varied or semi-structured data and machine learning. We recommend based on your actual workloads, not trends.
Yes. We build on the warehouse and cloud you already use where possible, and only suggest changes where they remove a real problem.
Let's talk about Data Engineering
A short call is usually enough to work out whether we can help and where it makes sense to start. No preparation needed.