Data engineering services that give every system one answer Tested pipelines and one data platform in your cloud, run by us or you
Data engineering services, from assessment to a running platform Everything between your systems and the reports, apps and agents that read them
How our data engineering consulting works Five steps, from tracing your numbers to pipelines that run every day
- Week 1Assess requirementsWe trace the numbers your teams argue about back to the systems they come from.
- Week 2Agree the modelA project, a team, or data engineering as a service that we run.
- Weeks 3 to 8Design and developThe platform and the first pipelines, one subject at a time, starting with the one that hurts.
- Before launchTest and QAEvery pipeline is tested, and every figure is checked against its source system.
- After launchSupportWe monitor each run, fix breaks early, and add the next source when you need it.
When data engineering services are the right fit And when a connector tool or a single report is enough
A good fit when
- Your systems give different answers to the same question
- Many reports, apps and agents need the same numbers, defined once
- Your data team spends its week fixing broken loads
Look elsewhere when
- You run two or three standard systems: a connector tool may be enough
- Your data is in one place and you need better reports: see analytics
- Nobody will own the data once it's built
Our data engineering services in detail What each service covers, and what you have when it's done
Data quality assessment
We start with the numbers that cause arguments: revenue that differs between the CRM and the ledger, or a customer count that changes by report. We trace each one back to its source, find where it splits, and list the fixes in order of cost.
You keep the map of your data, whether or not we build anything after it.
Data pipeline development and ETL or ELT
A pipeline moves data from a source system to where it's used, and changes it on the way. We build batch pipelines for nightly loads and streaming pipelines for data that can't wait, with ETL or ELT depending on where the work runs cheapest.
Every pipeline is code, kept in version control, tested before it changes, and run on a timetable with alerts when it fails.
- Ingestion from ERPs, CRMs, files and APIs
- Tests on every load, so bad data stops at the door
- Lineage from each figure back to its source
Data lake and data warehouse implementation
We build the store your data lands in: a data warehouse for structured reporting, a data lake for raw and varied data, or a lakehouse that does both. We work on Snowflake, Databricks, Microsoft Fabric, Azure Synapse, Amazon Redshift and Google BigQuery.
It runs in your own AWS, Azure or Google Cloud account, and your security team holds the encryption keys.
Data infrastructure modernization
Many companies still load data with scripts on a server, or report from a warehouse built 15 years ago. We move it to a modern platform one subject at a time, and run old and new side by side until the figures match.
For BAF Financial and Insurance, we reconcile each policy from three legacy systems into one record, and three in four policy records now migrate untouched.
Analytics and AI enablement
Once the data is joined and trusted, everything built on it gets easier. Dashboards tie to the ledger, machine learning models train on clean history, and AI agents read the same customer record as your people do, with the same permissions.
Questions about data engineering services What buyers ask us before they start
Why do I need data engineering consulting?
Because reports, apps and AI are only as good as the data under them. Data engineering replaces manual exports and fragile scripts with tested pipelines, so every team reads the same numbers.
How do I get started with data engineering services?
Bring the numbers your teams disagree on. We trace them to their sources, propose a plan and an engagement model, and start with the subject that costs you the most.
We know little about data science. Can we still work with you?
Yes. Most of our clients don't have a data science team. We choose the technology with you, build the platform, and can run it for you, so your people only use the results.
Can you build a data lake or data warehouse for us?
Yes. We design and build data lakes, warehouses and lakehouses on Snowflake, Databricks, Fabric, Redshift or BigQuery, in your cloud account and connected to the systems you run.
What's the difference between data engineering and data analytics?
Data engineering moves, joins and cleans the data. Data analytics uses it to answer questions, in dashboards, reports and models. Analytics needs engineering underneath it to be trusted.
Bring two numbers your systems disagree on
We'll trace both to their sources and show you where they split.