Data engineering

Reliable, observable pipelines and lakehouse architectures — including deep, hands-on work with your databases — built to scale and stay healthy in production.

SparkdbtApache IcebergSnowflakeDatabricks
unaflow · data-engineering
ExtractLoadTransformServeLakehouse
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less time-to-insight
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pipeline uptime
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source of truth
What we do

Pipelines your team can trust.

Batch and streaming, modelled and orchestrated — data that is correct, fresh and ready to use.

Batch & streaming ELT/ETL

Ingestion and transformation for both scheduled and real-time workloads.

Lakehouse architecture

Open, scalable storage on Iceberg, Snowflake or Databricks — one source of truth.

Data modeling with dbt

Well-tested, documented models that turn raw data into reusable datasets.

Observability & quality

Monitoring, tests and alerting so you catch issues before your stakeholders do.

How we work

A clear, proven process.

No mystery, no lock-in — a path you can follow from first call to running system.

1

Ingest

From every source

2

Transform

Clean, model, test

3

Store

Lakehouse

4

Serve

Ready for analytics

Why it matters

Reliability is the feature.

A data platform is only as good as the trust people place in it. We build pipelines that are observable, tested and resilient — so the numbers are right, every time.

  • Sleep-through reliabilityPipelines that recover, retry and alert.
  • Built to scaleFrom gigabytes to petabytes without a rewrite.
  • Trustworthy dataTested, documented and consistent everywhere.

Tools we go deep on

The right tools for your stack — these are where our expertise runs deepest.

SparkdbtApache IcebergSnowflakeDatabricks
Let's talk

Ready to get started?

Tell us about your data stack. We'll show you where it's fragile and how to make it solid.

Book a free discovery call