Book a call ↗

Data Engineering & Analytics

Data your models
can rely on.

Pipelines, lakehouses and dashboards with quality gates and lineage, so data is ready for models and for decisions.

Book a 45‑minute discovery call
← All services

What we deliver

Raw to curated.
Checked at every step.

We connect your systems and build the pipelines, lakehouses and warehouses that analytics and AI depend on.

On Databricks, Snowflake, BigQuery, Iceberg, ClickHouse or PostgreSQL, in your cloud or on-premises.
Solution scope

From source to decision

One governed path for your data.

  1. ConnectDatabases, business systems, APIs and IoT
  2. TransformBatch and streaming pipelines, dbt models
  3. StoreLakehouses and warehouses on open formats
  4. UseDashboards, analytics and model-ready tables
Quality gates, lineage and access control throughout

What you take away

  1. 01

    Pipelines that hold up

    Batch and streaming pipelines with contracts, monitoring and history preserved.

  2. 02

    A platform shaped to the business

    Raw, refined and curated layers on the platform you already use.

  3. 03

    Checks before anyone relies on it

    Hard-stop quality gates, lineage and access control.

  4. 04

    Data ready for AI and decisions

    Model-ready tables, plus Power BI or Looker dashboards built around real decisions.

Technologies we work with

Chosen for your requirements and your existing environment.

How we work

Built in stages.
Operated 24×7.

The team that builds it runs it, from the first decision through ongoing operation.

  1. 01 / Define

    Agree the outcome.

    Scope, constraints and success measures.

  2. 02 / Build

    Build it as code.

    Infrastructure, data or AI, in working stages.

  3. 03 / Validate

    Prove it on real work.

    Real workflows, real traffic, a rehearsed restore.

  4. 04 / Operate

    Run it 24×7.

    Monitoring, on-call, upgrades and improvement.

Explore our approach ↗

From engineering to impact

The value,
in practice.

A closer look at a related client engagement.

Manufacturing

Revenue reporting in days.

A manufacturer that could not tell whether revenue moved on price, volume or product mix.

Explore the project
Project outcome
Revenue reporting in days, not months.
Reporting in days.The reporting cycle moved from months to days.
Fewer data-quality issues.Change data capture and preserved history catch problems before reports do.
Built for volume.The lakehouse takes large daily sales volumes in its stride.
Manufacturing · individual engagementView case study
Also exploreModel-ready data, right after close. Cash operations in near real time. Content reporting, automated. One view of campaign performance.

Your next step

Start with the report
that takes longest.

Forty-five minutes on your workload, environment and constraints. You leave with a proposed first step.

Book a 45‑minute discovery call