4.9
Data engineering services for eCommerce
Pipelines that connect your store platform to the systems behind it, built and documented by engineers who work with eCommerce data every day.
Trusted by 700+ leading brands worldwide
Trusted by 700+ leading brands worldwide
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Certified to hold your customer data
A warehouse concentrates your order and payment records into one place, which raises the security bar. scandiweb is certified to ISO 27001 and ISO 27017, and holds PCI DSS compliance. Warehouse access and data retention are designed against those controls during the build.
What our data engineering work covers
Scope starts from the systems that hold your order data and the questions your business needs answered from them. Where event tracking is the weak link, our tracking setup team corrects that first.
Warehouse architecture
A warehouse sized against your volume and your team's skills, on platforms including BigQuery, Snowflake, or Databricks.
Ingestion pipelines
Because source systems export on different schedules, ingestion is built to pull each one reliably and retry when a load fails.
Order reconciliation
Order records from every source matched against one definition, with each discrepancy listed for your team to review.
Transformation and modeling
Raw source data transformed into modeled tables, and the logic is version-controlled where your engineers can read it.
Orchestration and monitoring
When a scheduled load fails, alerting fires before the reporting layer shows a gap that nobody downstream can explain.
Data quality tests
Checks on row counts and key fields execute on every single load, so a silent schema change is caught the day it happens.
Find out which of these your setup needs first
Why brands choose scandiweb for data engineering
23+ years in eCommerce
We have built these pipelines for online stores before, and we know which parts of the data usually end up disagreeing.
Certified for data at rest
scandiweb holds ISO 27001 and ISO 27017, and warehouse access is designed against those controls from the first build.
Your engineers can take it over
Pipeline code and models are documented and version-controlled. Your own engineers extend them without scandiweb’s involvement.
Built for what consumes it
Because the reporting and CDP specialists are in the same company, the warehouse is modeled for what will read from it.
Steps of a data engineering project
Source audit
We inventory every system holding order or customer records, check what each can export, and document where two of them disagree.
Architecture and platform
We size the warehouse against your volume and recommend a platform, with the cost of operating it stated before any build.
Ingestion build
Each source is connected with retries and schedule handling, and every load is logged so failures are visible immediately.
Transformation and reconciliation
Modeled tables are built, order definitions are agreed across systems, and any remaining discrepancy is reported as a visible figure.
Orchestration and handover
Scheduling and alerting go live, the code is documented, and your engineers are walked through extending the pipeline.
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Data warehouses we have built
Physical store and eCommerce order data merged into a single warehouse
120+ stores
Consolidated with eCommerce and ERP records
5 markets
Modeled with per-market differentiation
Anomaly alerts
Configured on the KPIs executives read
Learn more
A data layer built from scratch for a custom eCommerce platform
BigQuery
Warehouse unifying ten markets
Full funnel
Tracked across product and checkout
Seller KPIs
Reported in real time
Learn more
What clients say about working with scandiweb
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Frequently asked questions about data engineering
Which warehouse platform do you recommend?
That follows your data volume and which cloud you already pay for. We deliver on platforms including BigQuery, Snowflake, Redshift, and Databricks. The recommendation is written down with its operating cost before anything is built, so you can weigh it against what your team already knows.
How do you handle order data that disagrees between systems?
We establish one definition of an order with your finance and eCommerce teams, then model each source against it. Records that still disagree are reported as a reconciliation figure you can see, because a pipeline that hides the gap is worse than one that shows it.
What does a warehouse cost to operate?
Cloud cost scales with the volume you load and query. We put a monthly estimate in writing during the architecture stage, before any build work starts. Query patterns matter more than storage for most eCommerce datasets, which the modeling stage accounts for.
Can you work with our existing pipelines, or does everything need rebuilding?
Existing pipelines usually stay. We audit what is already in place, keep whatever is reliable and documented, and rebuild the parts that fail silently or that only one person understands. A full replacement is a decision you make after the audit.
Do we need a data engineer on our side?
How is this different from your BI and reporting work?
This page covers the layer underneath. We build the warehouse and the pipelines that fill it, along with the models on top. Dashboards and scheduled reporting are delivered by our dashboards and reporting team, often on the same project.
Talk to a data engineer
List the systems holding your order data and whether a warehouse is already in place. A specialist replies with what a source audit would cover.
Prefer to talk now? Book a call straight away, or email us at: [email protected]
Related services
Two systems, two different order counts? We can show you where they diverge
A specialist reviews how your sources connect today and what a reconciled pipeline would involve.
