“Distil Stack understood the operational complexity of commercial laundry services and helped shape a platform around our business processes. Bringing customer accounts, credits, laundry status, and service management together created a more connected digital experience.”
Data Engineering & MLOps
Data engineering & MLOps services
We connect your sources, model your data in a warehouse or lakehouse, and set up the pipelines and MLOps tooling that keep analytics and AI running without manual fixes.
A custom engineering engagement
We scope this service around your workflow, existing systems and acceptance criteria. The work starts with the problem and its constraints, then a written scope and price. A packaged solution is a different buying route: choose it when its defined deliverables already fit the job.
01The problem
Where things usually stall
Pipelines, warehouses and ML infrastructure that keep data and models fresh.
- 01
Reports that disagree
Finance and sales pull the same metric from different systems and get different numbers.
- 02
Pipelines held together by cron
One schema change upstream and the Monday dashboard is empty.
- 03
Models nobody can retrain
The person who trained the model left, and the steps to rebuild it lived in their head.
02Capabilities
What we deliver
- 01
Source integration
Connectors for SaaS tools, databases, files and event streams, with change data capture where it matters.
- 02
Warehouse and lakehouse design
Data models in Snowflake, BigQuery, Postgres or Databricks that match how the business asks questions.
- 03
Pipeline orchestration
Scheduled and event-driven pipelines with tests, retries and lineage.
- 04
Data quality monitoring
Freshness, volume and schema checks that alert the owner before a dashboard breaks.
- 05
ML pipelines and registries
Reproducible training, versioned models and one-click redeploys.
- 06
BI enablement
Semantic layers and dashboards your teams use without filing a ticket.
03Outcomes
What changes once it’s live
- 01
Traceable data movement
Pipelines with clear source ownership, transformations and destinations.
- 02
Failures that surface
Freshness checks, validation and alerts distinguish late data from genuinely empty data.
- 03
Reproducible model operations
Versioned datasets, deployment configuration and release records support controlled changes.
04Approach
How we deliver it
- 01
Map sources and consumers
Identify who produces each dataset, who uses it and which downstream decisions depend on its freshness.
- 02
Agree contracts and access
Define schemas, quality expectations, retention and permissions before building the pipeline.
- 03
Build for replay and recovery
Implement incremental processing, backfills, orchestration and validation that can handle delayed or duplicated events.
- 04
Make operations transferable
Document lineage, alerts, deployment steps and ownership so the pipeline can be maintained by your own team.
05Stack
Tools we use most
- Snowflake
- PostgreSQL
- Python
- Docker
- Kubernetes
- ClickHouse
- Kafka
- DynamoDB
- BigQuery
- Cassandra
- TimescaleDB
- MLflow
06Case studies
Featured success stories
Do we need a data warehouse?
If more than two systems feed your reporting or AI, almost always. We size it to your volume, and many clients start on managed Postgres or BigQuery.
Can you work with our existing BI tool?
Yes. We model the data once and connect it to whatever your teams already use.
Who owns the pipelines after launch?
You do. Everything runs in your cloud and lives in your repositories, documented, with runbooks for your team or ours.
How is the engagement priced?
We agree a fixed scope and price after reviewing the work, system dependencies and acceptance criteria. The pricing guide explains approved ranges for the engagement types it covers. A written proposal confirms what is included before a build starts.
How should the first delivery decision be made?
Identify who produces each dataset, who uses it and which downstream decisions depend on its freshness. Define schemas, quality expectations, retention and permissions before building the pipeline.
What should the acceptance criteria cover?
Pipelines with clear source ownership, transformations and destinations. Freshness checks, validation and alerts distinguish late data from genuinely empty data. Versioned datasets, deployment configuration and release records support controlled changes.






