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Data Engineering

Pipelines that fail loudly, a warehouse modelled on how the business actually thinks, and lineage you can follow from a dashboard back to the row it came from. Data incidents are rarely outages. They are silent degradations somebody notices a month later in a number that looks slightly off, which is why checks run with the pipeline rather than beside it. This is also the practice every AI feature quietly depends on: retrieval is only ever as good as what it retrieves over.

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Built with
  • dbt
  • Airflow
  • Kafka
  • Snowflake
  • DuckDB
How we run it

Data Engineering, step by step

The same four moves on every engagement of this kind. No discovery phase that bills for months before anything runs.

  1. Map the sources

    Every system holding the data gets catalogued, with who owns it and how far it can be trusted. Lineage starts here, not after the first bad number.

  2. Model

    We agree the shape the data needs to be in for the questions you actually ask, rather than copying whatever the source systems happen to emit.

  3. Build the pipeline

    Ingestion and transformation with tests on the data itself, so a bad upstream change fails loudly instead of quietly poisoning a dashboard.

  4. Monitor and hand over

    Freshness and quality checks that alert before anyone downstream notices, plus documentation your analysts can read without us.

What Data Engineering covers

Pipelines that fail loudly

Ingestion and transformation that tell you when something is wrong instead of quietly writing bad rows for a fortnight. Most data incidents are not outages; they are silent degradations somebody notices a month later in a number that looks slightly off.

What that means
  • Schema and freshness checks at every hop
  • Alerts on the data, not just the job
  • Backfills that are safe to re-run

A model of your business, not of your source systems

Warehouse design that reflects how the business actually thinks, so an analyst can answer a question without first learning which of four tables is the one people trust. Definitions get agreed once and written down.

What that means
  • Agreed definitions for the metrics that matter
  • One place each fact lives
  • Documented, and kept documented

Lineage and access built in from the start

Where every field came from, who can see it, and what breaks if it changes. Retrofitting this into a platform that already has users is far more expensive than building it in on day one.

What that means
  • Column-level lineage
  • Access by role, reviewed
  • Audit trail on the sensitive tables

The foundation AI features actually need

Retrieval is only as good as what it retrieves over. Getting the data correct, current and permissioned is most of the work in any copilot worth shipping, and it is the part that tends to be skipped.

What that means
  • Content chunked and kept current
  • Permissions carried through to retrieval
  • A benchmark for what good answers look like

Is your data ready for what you want to build on it?

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Where AI fits into Data Engineering

Every AI feature worth shipping sits on top of this practice, and it is the part that gets skipped. A copilot is only as good as what it reads, how current that is, and whether it respects who was allowed to see it.

  1. Content prepared for retrieval

    Documents chunked, embedded and kept current as the source changes, rather than a one-off index that silently goes stale a month after launch.

  2. Permissions that survive the pipeline

    Access rules carried from the source system through to the answer, so a model cannot surface a row the person asking was never allowed to see.

  3. A benchmark for good answers

    A held-out set of real questions with agreed answers, so retrieval quality is a number you can track rather than an impression from a demo.

Why teams pick us for Data Engineering

We start with the questions, not the tables

What decisions the data is supposed to support comes first. Modelling before that is how warehouses end up technically correct and practically unused.

Correctness over cleverness

A boring pipeline that is right every day beats an elegant one that is right most days. In this practice the failure mode is silent, which makes plain and checkable worth more than clever.

Your warehouse, your tooling

We work in the stack you already run wherever we sensibly can. Migrating a warehouse to suit a supplier is a cost with no return for you.

Handover is part of the work

Your analysts should be able to extend the model without us. If they cannot, we have built something that will decay the moment we leave.

What you receive

  • Versioned, tested transformation layer
  • Data quality dashboard with owned thresholds
  • Lineage graph from source to dashboard
  • Backfill and replay procedures

The stack we build this on

Ingestion and streaming

  • Kafka
  • Debezium
  • Fivetran
  • Airbyte
  • Change data capture

Transformation

  • dbt
  • Spark
  • Python
  • SQL
  • Great Expectations

Storage and warehouse

  • Snowflake
  • BigQuery
  • PostgreSQL
  • DuckDB
  • S3 and Parquet
  • pgvector

Orchestration

  • Airflow
  • Dagster
  • Prefect
  • Temporal

Governance and lineage

  • OpenLineage
  • Data contracts
  • Column-level lineage
  • Row and column access policies
  • Audit logging

Often paired with

Full-Stack & AI DevelopmentWeb, backend, LLM features and agents, built by one team.View practice AI Strategy & ConsultingWhere AI is worth applying, and where it is not.View practice Product & UX DesignInterface and flow design, built to be built.View practice
Questions

Before you get in touch

The questions that come up most on a first call about this practice, answered the way we would answer them on the phone.

It is the normal starting point. Every engagement begins with what is actually there rather than what the documentation claims, and that gap is usually the first useful finding.