Marketing data engineering for an ecommerce brand costs from about $1,000 for a first dashboard on one data source, and about $4,000 for a pipeline from one or two sources, to $30,000 and more for a data warehouse build. One ecommerce analytics consultancy puts its projects at $30,000 to $300,000. On top of the build, the brand pays a monthly bill for the tools that move, store and model the data. Brand Media Works does this work for ecommerce brands; the figures below are what other firms publish about their own.
What do consultancies charge for data engineering?
Few firms publish a price. The table sets out the ones in the Brand Media Works competitor register that do, with firms that work with ecommerce brands listed first. Each firm is described by the kind of work it does.
| Type of firm | Published price | Read on |
|---|---|---|
| Data analytics consultancy that sells Shopify reporting | One rate of $100 an hour, with most projects quoted as a fixed price First dashboard from one data source: about $1,000 and about 1 week Custom ETL from one or two sources with automated refresh: about $4,000 and about 2 weeks Custom ETL across around three sources with several reports: about $10,000 and about 4 weeks | 13 Oct 2026 |
| Ecommerce analytics consultancy | Ecommerce analytics consulting projects from $30,000 for small projects to $300,000 for the largest | 13 Oct 2026 |
| Data engineering consultancy | A proof of concept or pilot from $20k to $50k Several platform builds and legacy migrations delivered for under $100k | 18 Oct 2026 |
| Software development and data consultancy | Data warehouse consulting: $10,000 to $50,000 Basic warehouse, up to 5 internal sources with batch loading: $30,000 to $150,000 Medium warehouse, multiple sources with batch and real-time loading: $150,000 to $600,000 | 18 Oct 2026 |
| Software development firm with teams in Central and Eastern Europe | Basic data analytics solution: $35,000 to $100,000 Migration to a cloud data warehouse: $140,000 to $280,000 | 18 Oct 2026 |
| Data engineering and AI development firm | Data lake or data warehouse implementation: $100K to $500K | 18 Oct 2026 |
The range is wide because the firms are pricing different jobs. The first row prices small, fixed pieces of work: one dashboard, or one pipeline from a couple of sources into a reporting tool. The warehouse rows price a full platform, often with real-time loading and many internal systems such as ERP and CRM. For most ecommerce brands, the first project sits nearer the first row than the last.
What makes a data engineering project cost more?
Four things move the price more than anything else:
- The number of sources. Published prices step up from one source to two to around three, and a basic warehouse tier covers up to five. A typical DTC stack of Shopify, Meta Ads, Google Ads, Klaviyo and GA4 is already five.
- Batch or real time. Moving from daily batch loads to batch plus real-time is the step from a $30,000 to $150,000 warehouse tier to a $150,000 to $600,000 one in the published prices.
- Whether a warehouse is needed yet. A pipeline into a reporting tool is a weeks-long job. A warehouse build is a different scale of project: one firm puts implementation at six to nine months, starting from $70,000, with a proof of concept in two to three weeks.
- Requirements left until later. Security, data retention and access rules that are skipped at the start can add 20% to 40% to the original estimate, according to one development firm.
Fixed prices are common for small work. One firm that offers both models keeps fixed prices for small warehouses and fixed-scope engagements of up to four months, and moves larger builds to time and materials.
What do the tools cost each month?
The build is a one-off cost. The tools that move, store and model the data are a monthly one, and they are billed by the vendors directly. Prices below are from each vendor's own pricing pages, read on 18 October 2026.
| Job | Tool | How it charges |
|---|---|---|
| Moving data from Shopify, ad platforms and email into one place | Fivetran | By monthly active rows. The Free plan covers up to 500,000 monthly active rows for connections. On paid plans, each standard connection with between 1 and 1 million monthly active rows carries a $5 base charge. Fivetran's own Standard plan example prices a Google Ads connection with 88,240 rows at $48.67, Facebook Ads with 34,479 rows at $22.06 and GA4 with 21,993 rows at $15.88. |
| Moving data, alternative | Airbyte Cloud | By credits. Plans start at $20 a month with 5 credits included, and extra credits cost $5 each. |
| Storing and querying the data | Google BigQuery | By storage and data scanned. The first 10 GiB of storage and the first 1 TiB of query data processed each month are free. |
| Storing and querying, alternative | Snowflake | By compute credits and storage. An extra-small warehouse uses 1 credit an hour, billed by the second with a 60-second minimum. Snowflake's own cost example uses $2 a credit and $23 per TB of storage a month. |
| Modeling the data into reports | dbt | By seat. The free Developer plan has one seat. The Starter plan is $100 per user a month, for up to five developer seats. |
For a brand with a handful of sources and a few years of order history, the warehouse can sit inside the free tiers for some time, and the ingestion bill grows with the number of rows synced each month. Ask any consultant for a monthly tool-cost estimate alongside the build price, based on your own row counts.
Should a brand hire a consultant or a data engineer?
At published rates, a US data engineer bills $70 to $150 an hour. Business intelligence consultants are quoted at $120 to $250 an hour onshore, $20 to $100 nearshore and $15 to $60 offshore. A small fixed-price project, such as a two-source pipeline at about $4,000, costs less than one month of a data engineer's time at those rates (our arithmetic: 160 hours at $70 to $150 is $11,200 to $24,000). That makes it a cheaper way to find out what the data can do before committing to headcount.
The cost of doing nothing is the other side of the sum. 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 by hand, which at $80 to $120 an hour loaded cost comes to $160K to $1.2M a year. A smaller team spends less, but the pattern is familiar: finance, marketing and the agency each rebuild the same numbers in their own spreadsheets.
What should an ecommerce brand budget for a first project?
Start with one bounded project that answers one question, such as blended customer acquisition cost by channel or contribution margin by order. At published prices, a pipeline from one or two sources costs about $4,000 and takes about two weeks, and a build across around three sources costs about $10,000 and takes about four. A warehouse that brings in every channel and reconciles to finance is the next step, once the first project has shown which numbers the team actually uses.
Ask for three things in every quote: the fixed price and what it covers, the monthly tool cost at your current data volumes, and the hourly rate for changes after handover. Marketing data engineering sets out how Brand Media Works scopes this work. For how a data project fits a brand's wider AI budget, see how much an AI consultant costs for a $20M ecommerce brand, and for published prices across every kind of AI consulting work, see AI consultant cost in 2026.
Frequently asked questions
How much does a data engineer cost per hour?
Published US rates for a data engineer run from $70 to $150 an hour. One consultancy that sells Shopify reporting publishes a single rate of $100 an hour and quotes most projects as a fixed price.
Do we need a data warehouse before we can report across channels?
Not always. A pipeline from one or two sources into a reporting tool is a small project at published prices. A warehouse becomes worth it when the brand has several sources to join, needs history it controls, or wants the same numbers to feed finance, marketing and AI tools.
How long does a data engineering project take?
Published estimates run from about one week for a first dashboard to about two weeks for a one- or two-source pipeline and about four weeks for around three sources. A full data warehouse implementation is quoted at six to nine months by one firm.