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Data Engineering

Data modeling, ingestion, governance, and quality checks on scalable cloud platforms.

What's included

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Digital Qubits designs and builds data platforms that people can report from without arguing about whose numbers are right. The work covers modeling, ingestion, governance, and quality checks on Azure, AWS, and Google Cloud. We do it as a delivery engagement, or we add the data engineers your team is missing.

Most analytics trouble starts upstream of the dashboard. A pipeline drops records and nobody notices until a meeting. Two reports use the same word for different totals. A field that used to mean one thing now means another, and the change was never written down. The warehouse can be new and the distrust can still be old. We start by tracing a few important numbers back to the systems that produced them, and by naming who is allowed to decide what correct means.

Ingestion is where quality is cheapest. Each dataset is checked for expected formats, required fields, acceptable ranges, duplicates, and freshness before it moves downstream. A load that fails early is a short investigation. A load that fails inside a monthly close is a long one. We put those checks on every run, with an alert when something drifts, rather than a quarterly audit that discovers the problem after decisions have already been made.

Ownership is part of the design, not a document filed afterward. Important datasets get a named owner. Lineage is recorded so a number can be walked back through the transformations that touched it. That is what turns a dispute about a figure into a specific question about a source, a rule, or a load. Governance does not have to be heavy. It has to cover the datasets behind the decisions you actually make, and then expand from there.

The platform is chosen for the workload. Some organizations need a warehouse and a small set of trusted tables. Others need a lake for raw history plus a curated layer analysts can use. We work on Azure, AWS, and Google Cloud. The aim is fewer copies of the truth, and a place people can use without a private translation.

If the numbers in your reports do not agree, or you are standing up a platform and want it trustworthy from the first load, write to us. A short description of the sources and the decision the data has to support is enough to start.

// What's included

From raw data to numbers people can stand behind.

  • 01Data modeling and ingestion from the systems you already run
  • 02A named owner for important datasets
  • 03Quality checks for formats, required fields, ranges, duplicates, and freshness
  • 04Lineage, so a number can be traced to its source
  • 05Cloud warehouses and data lakes on Azure, AWS, or Google Cloud

// How the work runs

From the first conversation through support.

When we deliver the work, every engagement follows the same path.

  1. 01

    Gather requirements

    We start by listening: goals, constraints, the systems you have, and what a useful result looks like.

  2. 02

    Design and prototype

    We shape the approach early and prototype so the trade-offs are visible before the full build.

  3. 03

    Build

    Hands-on engineers do the work in short iterations, with you close to the decisions.

  4. 04

    Test

    Requirements are tested against real user expectations early, not only at the end.

  5. 05

    Deploy

    We plan the cutover, train the people who will use what we built, and go live with a checklist.

  6. 06

    Support

    After go-live you still work with the people who did the work, for questions and stabilization.

// Related practices

Often paired with this work.