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Data Engineering

Pipelines that move data reliably from your product and third-party systems into a warehouse, with the quality checks, schema management and reporting that make the numbers trustworthy.

The dashboard shows a number. Nobody can say where it came from or whether it is still being updated.

Data pipelines

Batch and streaming pipelines from transactional databases, event streams, files and APIs into a warehouse or lake. Every job can be re-run safely, every source is checked for changes, and every pipeline raises an alert when data stops arriving.

Orchestration and transformation tooling is chosen to match your cloud and the team that will run it afterwards, not the other way round.

  • Database change capture

  • Streaming

  • API and file ingestion

  • Orchestration

  • Alerts when data stops

One place for all your data

A warehouse that analysts can use without asking an engineer: dimensional or wide-table models designed around the questions the business asks, with documented definitions for every metric.

For larger or less structured data, a lake on S3 or Cloud Storage with a query layer on top, partitioned so queries stay cheap.

  • Metric definitions

  • Tested transformations

  • Lake storage

  • Access control

  • Cost control

Numbers you can trust

Bad data costs more than no data because people act on it. We add checks when data comes in and after it is transformed: schema, missing values, ranges, records that point to nothing, row counts against the source, and business rules your analysts define.

Failures stop the pipeline or quarantine the batch, and someone is told, before a dashboard shows the wrong number.

  • Schema checks

  • Row counts checked

  • Business rules

  • Bad batches held back

  • Quality dashboards

Analytics and reporting

Dashboards and reports on top of the warehouse, in the BI tool your team uses, with metrics defined once and reused. We also build customer-facing analytics into products where reporting is a feature.

Where a question needs a model rather than a report — forecasting, anomaly detection, scoring — see machine learning on the AI page.

  • BI dashboards

  • Embedded analytics

  • Scheduled reports

  • Self-service datasets

  • Team handover

Questions we get on the first call

It depends on where the rest of your stack is and the query patterns: Redshift or Snowflake on AWS, BigQuery on GCP, and PostgreSQL for teams whose data fits. We will not recommend a platform you cannot staff.

Yes. We start by mapping what runs, what it produces and what breaks, add monitoring, then fix or rebuild in priority order.

Data is classified as it comes in, sensitive fields are masked, access by team and sensitivity, and audit logs on the warehouse.

Four ways to
work with us

ecommerce shipping solutions

A defined project

A defined integration or product, priced and scheduled up front, delivered with the test suite and infrastructure code you keep.

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Engineers on your team

Engineers who join your team, your code and your daily meetings for as long as the roadmap needs them.

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Ongoing support

We keep the integrations you run working: monitoring, vendor updates, new connections and month-end support.

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Taking over existing systems

A system or integration nobody wants to touch: we review it, make it stable, then build on it.

What clients say

Tell us which systems
you run.label_leaf

An engineer replies within one business day, with questions rather than a pitch.

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