Data & AI
Data Engineering Services
Build the data foundation your business can grow on: reliable pipelines, well-modelled warehouses and trustworthy data, designed and run by a founder-led team with a senior engineer in charge.
Talk to the foundersWhat you get with Authect's data engineering services
Every report, forecast and AI feature depends on data arriving on time, in the right shape and correct. We design and build the systems that make that happen, across the whole data lifecycle: from an honest audit of what you have, through strategy and architecture, to pipelines, storage, quality checks and day-to-day operations.
Data engineering is where we start most analytics, business intelligence and machine learning work, because the foundations decide how far everything else can go. Each project is led by a senior engineer, with the founders involved from the first workshop, and every pipeline and warehouse lives in your accounts from day one.
What's included
Data engineering services covering the entire data lifecycle
Pick the pieces you need now and add the rest as you grow. One senior engineer keeps the whole architecture coherent, whichever you start with.
Data audit
A review of your data sources, infrastructure, tools, policies and processes at every stage of the lifecycle, with a clear list of what works, what's at risk and what to fix first.
Data strategy
A plan for managing your data as an asset, with a roadmap from where you are today to where you want to be, prioritised by value and effort.
Explore the serviceData management, BI and insights
Reports and dashboards that turn raw data into answers, built on models your teams can trust and explore for themselves.
Explore the serviceData monetisation
Finding new value in the data you hold, from smarter products and services to data you can share or sell responsibly.
DataOps
Version control, automated testing and continuous delivery for pipelines and data models, so changes ship quickly without breaking reports downstream.
Explore the serviceData migration and cloud adoption
Moving data between vendors, or from on-premises warehouses to cloud platforms, with continuous checks so nothing is lost or changed on the way.
Explore the serviceData governance and quality
Policies, ownership, access controls and automated quality checks that keep data secure, compliant and fit for use.
Data warehouses and lakes
Storage designed around how your data is used, from a single warehouse to a lakehouse combining structured and unstructured data.
Explore the serviceBig data solutions
Distributed processing and streaming for datasets and event volumes that outgrow a traditional database.
Performance optimisation
Finding the bottlenecks in slow queries, pipelines and storage, then fixing them to cut run times and cloud costs.
Give your teams data they can rely on, every morning.
Contact usService benefits
The benefits of data engineering services
- 01
Get more value from your data
A single, well-organised foundation replaces scattered spreadsheets and exports, so data from every part of the business can finally be combined and analysed together.
- 02
Make decisions on facts
When the numbers are consistent and up to date, leadership spends meetings deciding what to do, not arguing about whose figures are right.
- 03
Support growth without rework
Pipelines and storage designed to scale mean new products, markets and data sources can be added without rebuilding what's already there.
- 04
Run efficient data processes
Automated, monitored pipelines take over manual exports and overnight scripts, cutting processing time and the hours your team spends fixing failures.
- 05
Keep data accurate and secure
Quality checks, clear ownership, access controls and encryption protect sensitive information and catch problems before anyone builds a report on them.
How we work
How we deliver data engineering services
Five stages take you from scattered sources to a foundation that runs itself. A founder and a senior engineer are with you at every stage, and you see working results early rather than a big reveal at the end.
Why Authect
Why choose Authect for data engineering
- 01
End-to-end delivery
We handle the whole path from source systems to dashboards and models, so the pieces fit together and there is one team accountable for the result.
- 02
Built around your industry
We shape every data model around how your business actually works and the rules it has to follow, rather than forcing it into a generic template.
- 03
Designed for the people who use it
Data only helps when people can use it. We design reports and data products with their users, so the insights are clear and easy to act on.
- 04
Yours from day one
Code, pipelines and infrastructure as code live in your repositories and cloud accounts, documented so your own team can take over whenever you choose.
FAQs
Data engineering FAQs
What is data engineering?
Data engineering is designing, building and running the systems that collect, store, transform and deliver an organisation's data. It covers pipelines that move data from source systems, warehouses and lakes that store it, and the quality checks and governance that keep it trustworthy, so analysts, dashboards and models always have reliable data to work with.
How does data engineering support business growth?
Growth adds systems, customers and data. Good data engineering means that extra data becomes useful quickly instead of piling up in silos. It shortens the time from a question to an answer, makes new analytics and AI projects cheaper to start, and keeps reporting consistent as the business changes.
What's the difference between data engineering and data science?
Data engineers build and maintain the infrastructure that makes data available and reliable. Data scientists use that data to analyse, predict and build models. The two work closely together, and in practice most data science projects spend a large share of their effort on data engineering.
How do I know if my business needs data engineering services?
Common signs are reports that take days to prepare by hand, teams with different numbers for the same metric, pipelines that fail silently, slow or expensive queries, and AI ideas that stall because the data isn't ready. If any of these sound familiar, a short audit will show where to start.
What technologies are typically used in data engineering?
We work with cloud warehouses and lakehouses such as Snowflake, Databricks, BigQuery and Amazon Redshift, transformation tools like dbt, orchestrators like Apache Airflow, streaming with Apache Kafka, Python and SQL, and the data services on AWS, Microsoft Azure and Google Cloud. We recommend what fits your data, team and budget.
What are ETL and ELT?
Both describe how data gets from source systems into a warehouse. In ETL (extract, transform, load), data is transformed before it is loaded. In ELT (extract, load, transform), raw data is loaded first and transformed inside the warehouse, which suits modern cloud platforms and keeps the original data available for new uses.
How does data engineering relate to cloud migration?
Moving data to the cloud is often the first step in a data engineering project, and the best moment to fix structure and quality. We plan the migration together with the target architecture, so you don't just copy old problems into a new platform.