Data engineering is the work of building and running the systems that collect data from where it is created, move it to one place, clean and reshape it, and keep it reliable enough to report on and build with. In a business it sits between the source systems, such as an online store, ad platforms and a CRM, and the people and tools that use the data, such as analysts, finance, dashboards and AI models. Brand Media Works does data engineering for ecommerce brands, so the examples below come from that world.
What does data engineering include?
Data engineering covers four stages, plus the work that keeps them trustworthy:
- Ingestion. Pulling data out of source systems on a schedule, usually through their APIs. For an ecommerce brand that means orders from Shopify, spend from Meta and Google Ads, and results from Klaviyo.
- Storage. Keeping all of it in one place, usually a cloud data warehouse such as BigQuery, Snowflake or Redshift, with the full history rather than only what each platform still shows.
- Transformation. Reshaping raw tables into agreed definitions: one customer record per person, revenue net of refunds and discounts, spend in the same currency and time zone as sales.
- Serving. Making the clean tables available to the tools that use them: dashboards, finance reports, audience lists sent back to ad platforms, and AI models.
Around all four sit orchestration, which runs each step in the right order and retries failures; data quality checks, which catch a missing day or a doubled order before anyone reports on it; and access control, which decides who can see customer data.
What is the difference between ETL and ELT?
ETL stands for extract, transform, load: data is cleaned and reshaped before it lands in the warehouse. ELT reverses the last two steps: raw data is loaded first and transformed inside the warehouse. ELT has become the common approach because cloud warehouses are cheap to store data in and fast to query, and keeping the raw data means a definition can be changed later without pulling everything again.
Modern ingestion tools also only move what has changed. Fivetran, for example, bills by monthly active rows: a record synced 30 times in a month counts once, and if a table of 120 rows is re-imported with 20 new rows and 20 changed rows, only 40 count. Transformation then happens in the warehouse, often in a modeling tool such as dbt.
How is data engineering different from data analytics and data science?
| Discipline | Main question | Typical output |
|---|---|---|
| Data engineering | Is the data complete, correct and in one place? | Pipelines, a warehouse, clean modeled tables |
| Data analytics | What happened, and why? | Dashboards, reports, analysis for decisions |
| Data science | What will happen, and what should we do? | Forecasts, models, experiments |
The three depend on each other in that order. An analyst who spends the week reconciling exports is doing data engineering by hand, and a forecasting model trained on inconsistent data will be wrong with confidence.
Why does data engineering matter for a business?
Because the alternative is people rebuilding the same numbers by hand. One data engineering firm estimates that at most organizations it works with, 8 to 20 people spend 4 to 8 hours a week reconciling data manually, which at $80 to $120 an hour loaded cost comes to $160K to $1.2M a year. A data consultancy puts it more bluntly from its own experience: nine out of ten clients say their data is ready for analysis, but only one in ten truly is.
It also matters more now that teams want to use AI on their own data. A model that answers questions about customers, orders or campaigns can only be as accurate as the tables it reads, which is why data engineering usually comes before any serious AI build.
What does data engineering look like for an ecommerce brand?
For a typical DTC brand, the first data engineering project connects Shopify and the main ad platforms into a warehouse, agrees one definition of revenue and one of acquisition cost, and keeps the history that Meta and Google Ads drop after 37 months. Data engineering: a guide for ecommerce brands walks through the sources, the stack and where to start, and how much marketing data engineering costs sets out published prices. Marketing data engineering describes how Brand Media Works does this work.
Frequently asked questions
Is data engineering the same as ETL?
No. ETL, or ELT, is one part of data engineering: moving and reshaping data. Data engineering also covers storage, modeling, data quality, orchestration and access control.
Do small companies need data engineering?
Once a company runs on more than two or three systems that each report their own version of the same numbers, yes, even if it starts as one small pipeline rather than a team.
Is writing SQL data engineering?
It can be. SQL is the main language of the transformation step, so a lot of data engineering is SQL. Writing a one-off query for a report is closer to analytics; building tested, scheduled transformations that other reports depend on is data engineering.