CodiotFree estimate
Data engineering

Data engineering services

Pipelines, warehouses, and reporting you can trust. The unglamorous plumbing that decides whether your analytics and AI are worth anything.

Review your data platform

What is data engineering?

Data engineering is the work of moving data from the systems that create it into a place where it can be trusted, queried, and acted on. Codiot builds the pipelines, models, and quality checks underneath reporting and AI, for companies whose numbers currently disagree depending on who pulls them and which spreadsheet they used.
Batch or streaming

How fresh does the data actually need to be?

BatchStreaming
FreshnessMinutes to hoursSeconds
ComplexityLower; easier to reason about and rerunHigher; ordering and late data must be handled
Cost to runLowerHigher, and always on
Best forReporting, finance, most analyticsFraud checks, live dashboards, alerting
Failure recoveryRerun the batchReplay the stream, if you designed for it
Capabilities

What we build.

Pipelines and ingestion

Moving data out of applications, databases, and third-party APIs on a schedule that matches how the business actually uses it.

Warehouse and modelling

A data model people can query without a tribal-knowledge briefing, with the definitions of core metrics written down and enforced.

Data quality and testing

Automated checks for missing values, broken joins, and silent schema changes, because the expensive failure is the one nobody notices.

Reporting and BI enablement

Dashboards built on governed data, so two teams asking the same question get the same answer.

AI and analytics readiness

Structuring and documenting data so retrieval, models, and reporting have something reliable to stand on.

How it works

How it works with Codiot.

  1. Map the sources first

    Which systems own which fields, how often they change, and where the current numbers diverge. Most data projects fail here, not in the tooling.

  2. Model before piping

    Agree the core entities and metric definitions up front, so the warehouse encodes decisions rather than postponing them.

  3. Ship one trustworthy dataset

    One domain, fully tested and documented, in production and in use. Then expand, rather than boiling the lake for six months.

Why Codiot

Stack we use, and why teams choose us.

PostgreSQLSnowflakeBigQuerydbtAirflowPythonAWSAzure
  • ·Senior engineers only, no hand-offs to juniors mid-project
  • ·Overlap hours with US/EU time zones
  • ·Weekly demos and a single point of contact
  • ·Code you own, documented and tested
FAQ

Common questions, answered plainly.

What is data engineering?
Data engineering is building the systems that collect, move, clean, and organise data so it can be used. It covers ingestion pipelines, the warehouse or lake the data lands in, the models that shape it into usable tables, and the tests that keep it correct. Analytics and AI are the visible layer; data engineering is what makes them trustworthy.
How is data engineering different from data science?
Data engineering builds and maintains the infrastructure and datasets. Data science analyses those datasets to answer questions or build models. In practice, teams that hire data scientists before doing the engineering find them spending most of their time cleaning data by hand rather than analysing it.
Do we need a data warehouse or is a database enough?
If reporting queries are slowing your production database, if you need data from several systems in one place, or if teams disagree about basic numbers, a warehouse earns its cost. If you have one application, modest volume, and reporting that runs fine today, adding a warehouse is premature.
How long does a data engineering project take?
A first trustworthy dataset in production usually takes weeks rather than months, provided the source systems are accessible and someone can settle metric definitions quickly. Full platform work runs longer and is best delivered domain by domain rather than as one large programme.
Can you work with our existing warehouse?
Yes. Most engagements start with what exists: reviewing the models, tests, and pipelines already in place, then fixing the parts that cause distrust before adding anything new.
Related

Explore related work.

Custom software developmentCloud and DevOps servicesData migrationLendingContact
Start

Let's talk about data engineering.

Tell us what you're building. We'll reply within two business days with an honest take on scope, timeline, and cost.

Get a free estimate