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Data Engineering
Ingestion, orchestration and pipelines with tests and monitoring built in, running in your cloud and owned by your team.
Where pipelines break
Most pipeline work starts from one of these. If yours is different, say so on the first call.
Reports break when a source changes its schema, and the first warning is a wrong dashboard.
Schema, freshness and volume tests run on every pipeline, and a failed test alerts the owning team.
Pipelines live in scheduled scripts that one person understands.
We move them into version-controlled orchestration with code review, CI/CD and a runbook for each job.
Data lands once a day, but the business needs some of it within minutes.
We add streaming ingestion where a use case needs it and keep batch where it is cheaper to run.
Connecting a new source takes weeks.
Reusable ingestion patterns and managed connectors, so adding a source means configuring a job.
What we build into every pipeline
Engagements are scoped from these lines. The written plan after the assessment says which apply to you.
Batch and streaming pipelines
Ingestion from databases, SaaS APIs, files and event streams, with incremental loads and late data handled.
You receive
- Pipeline code in your repository
- Source-to-target mapping
Orchestration and CI/CD
Scheduling, dependencies, retries and backfills in Airflow or the orchestrator you run, deployed through pull requests.
You receive
- Jobs under version control
- CI/CD pipeline and environments
Data quality tests
Schema, freshness and volume checks on every pipeline, plus the business rules your team agrees.
You receive
- Test suite that runs on every change
- Data contracts for critical sources
- Alerts routed to owners
Monitoring and handover
Run history, failure and cost views from day one, and documents your team can run the platform with.
You receive
- Runbook per pipeline
- Architecture diagram
- Recorded training
A data contract for each critical source
Critical sources get a written contract: the schema, the checks and how fresh the data must be. A failed check alerts the owning team, and a breaking change stops the deploy.
An illustrative example, not client data.
Tools on the pipeline path
We work in the tools you already run where they fit. These are the ones our engineers use most on pipeline work.
Listing a technology does not imply a vendor partnership or certification.
Orchestration
- Apache Airflow
- Azure Data Factory
Ingestion & streaming
- Fivetran
- Apache Kafka
Processing & transformation
- dbt
- Apache Spark
- Apache Flink
Quality & monitoring
- Great Expectations
- Monte Carlo
Delivery
- GitHub Actions
- Terraform
Questions about data engineering
Not answered here? Ask in the request form.
Do you work with the orchestrator we already run?
Yes, where it fits. We extend what you run today and only propose a move when the assessment finds a clear reason, which goes into the written plan.
Batch or streaming?
Whichever the use case needs. Streaming costs more to run, so we use it where data has to arrive within minutes and keep batch everywhere else.
Who is on call once the pipelines are live?
You choose. Your team takes over with the runbooks, or we keep running the pipelines with you on agreed support hours.
Can you take over pipelines another team built?
Yes. The assessment starts with what already runs: we read the code, the run history and the failure log, then say what to keep, what to fix and what to replace.
Who owns the code?
You do. Pipelines live in your repositories and data stays in your cloud, in open formats.
Discuss your pipelines
Tell us which sources, jobs and deadlines you are dealing with.
How it runs: a call (30 minutes with a senior engineer), an assessment and written plan, the build with weekly demos, then handover or run. Our delivery standard