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Data

Engineering

AI models are only as good as the data that trains and feeds them. Decisions are only as confident as the information they’re based on. Our Data Engineering practice is built on deep AWS and Databricks expertise, applied across the full spectrum of modern data challenges: from migrating legacy estates and engineering reliable pipelines to building the AI-ready data foundations your organisation needs to compete. We don’t just move data. We engineer it for trust, performance and scale.

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The Challenge

Our Approach

WHO IT’S FOR

HOW WE DELIVER

OUTCOMES & BENEFITS

PROVEN RESULTS

FAQS

The

Challenge

Most organisations are sitting on enormous amounts of data but struggling to extract reliable, actionable value from any of it.

AI Ambitions Are Being

Held Back by Data Readiness

The single biggest barrier to successful AI adoption isn’t the model – it’s the data. Poor quality, incomplete lineage and inadequate governance derail AI initiatives before they reach production.

Gartner estimates that poor data quality costs organisations an average of $12.9 million per year – a figure that grows significantly as AI dependency increases

Data Estates Are Complex,

Fragmented and Difficult to Trust

Legacy data architectures built for a pre-AI world are increasingly unfit for purpose: siloed, inconsistent and expensive to maintain

Only 3% of companies’ data meets basic quality standards, meaning the vast majority of data-driven decisions are built on foundations that can’t be fully trusted (Harvard Business Review)

Data Costs Are Growing

Faster Than Data Value

Unmanaged data platform sprawl, inefficient pipelines and over-provisioned infrastructure drive costs upward while delivering diminishing returns.

Without FinOps governance applied to data and AI workloads, organisations routinely spend 30-40% more on data infrastructure than the value it generates justifies

The cost of inaction

Organisations that treat data as an afterthought – or tolerate fragmented, ungoverned data estates – pay a heavy price across cost, compliance and competitive advantage.

average annual cost of poor data quality to an organisation (Gartner)

$ 0 m

of companies' data meets basic quality standards (Harvard Business Review)

0 %

of AI projects that cite data readiness as their primary barrier to reaching production

0 %

Our

APPROACH

We engineer data platforms that are trustworthy, performant and AI-ready. And we combine deep AWS and Databricks expertise with a quality-first approach that ensures your data estate is an asset, not a liability.

What Makes Us

Different

AWS AND DATABRICKS EXPERTISE

Deep, hands-on experience across the two platforms that define modern enterprise data engineering. Your data estate is built on foundations that scale, integrate and evolve with your business and AI ambitions.

QUALITY-FIRST DATA ENGINEERING

Drawing on our QE heritage, we embed data quality assurance throughout every pipeline and platform we build – not as an afterthought, but a core engineering discipline that ensures the data powering your decisions and AI models can be trusted.

AI-READINESS BY DESIGN

We build data platforms with AI in mind from the start, – architecting for the vector storage, feature engineering, model serving and governance requirements that production AI demands. You can be confident your data estate is ready when your AI ambitions are.

FINOPS FOR DATA WORKLOADS

Focus on business outcomes, not just technical Data and AI infrastructure costs can spiral quickly without the right governance. We apply FinOps principles specifically to data and AI workloads – ensuring your platform investment is right-sized, transparent and delivering measurable return.

SEAMLESSLY INTEGRATED

We work within your existing cloud environment, data tooling and delivery teams to accelerate data capability without disrupting the operations that depend on it.

Platforms, Tools

& Practices

TOOLS & TECHNOLOGIES

Data Services: S3, Redshift, Glue, Lake Formation, Kinesis, Athena, EMR, DataZone, SageMaker Feature Store

Delta Lake, Unity Catalog, Databricks Workflows, MLflow, Databricks SQL, Auto Loader

Glue Data Quality

Actions

CloudWatch

METHODOLOGIES

We follow the AWS Well-Architected Framework for Analytics, the Databricks Lakehouse architecture principles and Data Mesh design patterns where appropriate. And everything is underpinned by our Quality-Led and FinOps-informed approach to data platform delivery.

What's

included

DATA PLATFORM

AI-READINESS ARCHITECTURE

Ensuring your data platform is ready for the AI ambitions you’re building towards. We assess your current estate against AI-readiness criteria – covering data quality, lineage, governance, vector storage and feature engineering – and design the target architecture your AI initiatives need to reach production with confidence.

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DATA MIGRATION

& MODERNISATION

Moving from fragmented, legacy data estates to modern, scalable lakehouse architectures on AWS and Databricks. We plan, design and execute migrations that protect data integrity, eliminate technical debt and build the performant, governed foundations your business needs to extract real value from its data.

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DATA PIPELINE

ENGINEERING

Reliable, scalable data pipelines engineered for the demands of modern analytics and AI workloads. We design and build batch and streaming pipelines on AWS and Databricks. And embed quality gates throughout to ensure data arrives accurate, complete and on time, every time.

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DATA QUALITY

ENGINEERING

Drawing directly on our QE heritage, we apply software engineering discipline to data quality – designing and implementing automated quality frameworks that monitor, validate and alert across your entire data estate. Because the confidence you place in your data should be earned, not assumed. requirements.

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DATA GOVERNANCE

& COMPLIANCE

Building the policies, controls and tooling your organisation needs to govern data responsibly. We implement governance frameworks using AWS DataZone and Databricks Unity Catalog – covering data lineage, access controls, cataloguing and compliance with GDPR, and industry-specific regulatory requirements.

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FINOPS FOR DATA

& AI WORKLOADS

Applying financial governance and engineering rigour specifically to your data and AI infrastructure. We identify waste, right-size compute and storage, implement cost allocation frameworks, and provide the visibility needed to ensure every pound spent on data and AI workloads is justified by the value it delivers.

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Who Is This

Service For?

We work with organisations that have recognised their data estate is either their greatest competitive advantage or their biggest barrier to becoming one. The difference is how it’s engineered.

COMPANY SIZE

Scale-ups

Mid-Market

Enterprise

Maturity Level

Building a data platform

Migrating legacy data estate

Scaling analytics capability

Preparing data for AI

I need to Modernise infrastructure

Powering an AI future

DATA & AI LEADERSHIP

CHIEF DATA OFFICER / HEAD OF DATA

Data leaders responsible for building and governing an enterprise data estate that the business can trust – and that’s ready to power the AI initiatives the organisation is investing in.

Data platform strategy

Strategic transformation

AI-readiness

Developer experience

I need to scale reliably

TECHNOLOGY LEADERSHIP

CHIEF TECHNOLOGY OFFICER

CTOs who understand that data infrastructure is the foundation every AI and analytics ambition is built on and need a partner with the AWS and Databricks depth to get it right.

Platform architecture

Cloud-native data engineering

AI infrastructure readiness

I need to deliver value faster

Increase productivity

FINANCIAL LEADERSHIP

CHIEF FINANCIAL OFFICER / HEAD OF FINANCE

Finance leaders who need visibility and control over data and AI infrastructure spend – ensuring platform investment is governed, optimised and delivering measurable return.

Data platform cost governance

FinOps for AI workloads

ROI on data investment

I need to increase efficiency

Accelerate delivery

DELIVERY LEADERSHIP

HEAD OF ENGINEERING / ENGINEERING MANAGER

Engineering leaders responsible for building and operating data platforms who need proven expertise embedded directly into their teams to accelerate delivery without accumulating data debt.

Pipeline reliability

Data quality maturity

Developer productivity

scaleDELIVERY

Our proven five-phase

methodology

DISCOVER

Thorough discovery of your current state, challenges, objectives and constraints – establishing a shared understanding before a single recommendation is made.

Deliverables

Design

Translating discovery into a clear, agreed approach – covering target operating model, success criteria, tooling strategy and a prioritised roadmap aligned to your business goals.

Deliverables

DELIVER

Structured, phased execution led by our expert consultants – drawing on our proven asset library of templates, frameworks, checklists and process flows to accelerate delivery without cutting corners.

Deliverables

EVOLVE

Continuous improvement, knowledge transfer and long-term partnership – refining performance, embedding capability into your teams and ensuring you remain ahead of the curve.

Deliverables

INFORM

At every phase, we maintain a consistent thread of transparency and insight. You receive high-quality, actionable data on progress, risks and outcomes – not vanity metrics or status updates that obscure more than they reveal. Because confident decisions require confident information.

Deliverables

Outcomes & Benefits

Measurable impact across data quality, AI readiness, cost and platform performance

faster time to insight with modern lakehouse architecture versus legacy data warehouse approaches

0 x

reduction in data infrastructure costs through FinOps governance and right-sizing

0 %

reduction in data quality incidents with automated quality engineering embedded in pipelines

0 %

Business Benefits

DATA YOUR ORGANISATION CAN TRUST

Automated quality engineering and governance built into your platform means the data powering your decisions, reports and AI models is accurate, complete and reliable – not a source of doubt and rework.

AI INITIATIVES THAT REACH PRODUCTION

An AI-ready data platform removes the most common barrier between AI experimentation and real business value, giving your models the high-quality, well-governed data they need to perform at scale.

DATA COSTS THAT ARE JUSTIFIED AND CONTROLLED

FinOps governance applied to data and AI workloads means your infrastructure investment is transparent, right-sized and tied directly to the value it delivers, not growing unchecked in the background.

A PLATFORM THAT GROWS WITH YOU

Modern lakehouse architecture on AWS and Databricks is built to scale. It can handle growing data volumes, new AI use cases and evolving regulatory requirements without requiring a costly rebuild every few years.

Technical Benefits

LAKEHOUSE ARCHITECTURE FOR AI AND ANALYTICS

Delta Lake and Databricks Unity Catalog provide the unified, governed foundation that modern AI and analytics workloads demand – combining the flexibility of a data lake with the reliability and performance of a warehouse.

AUTOMATED DATA QUALITY GATES

Quality checks embedded throughout every pipeline validate completeness, accuracy and consistency at every stage. Data issues are caught before they reach consumers, models or reports.

END-TO-END DATA LINEAGE AND GOVERNANCE

Full visibility of where your data comes from, how it moves and who has access to it – enabling confident compliance, faster root cause analysis and the auditability that regulated industries demand.

CONTINUOUS PLATFORM INTELLIGENCE

Real-time visibility of pipeline health, data quality metrics and infrastructure cost across your entire data estate provides actionable insight that keeps your platform performing and your stakeholders informed.

Proven Results

Real transformations delivering measurable business impact

Health Sector

Challenge

A major pharmaceutical company had a hosting vendor that was expensive and slow-moving. The platform ran in only a single data centre, and so disaster recovery options were limited.

Solution

We made use of Terraform and Puppet scripts for provisioning AWS resources according to good security and operational practices. For each tenant we run a number of services on traditional autoscaled and load balanced EC2 instances, as well as some SQS queues.

Read Full Case Study

Essentia Analytics

Challenge

Essentia wanted to ensure their infrastructure on AWS was easily scalable and reliable, with minimal blast radius from any security issue that might arise.

Solution

We used AWS Landing Zone to bootstrap the account structure and establish a security baseline across the estate. We then integrated it with Terraform, which is used for day-to-day operations.

Read Full Case Study

What Our

Clients Say

Hear from engineering leaders who have transformed
their organisations with Scale Factory.

We’ve had a tremendous experience with Scale Factory. Their work quality is outstanding, they deliver on time and really go the extra mile to meet our needs – even if we don’t know them yet. They are very adaptive and flexible from a scope, management and procurement perspective. We learn a lot every day and it’s a real pleasure to work with the team.

Max Burian

Dectris Product Manager

Frequently Asked

Questions

Why Databricks alongside AWS rather than AWS-native tooling alone?

Databricks provides capabilities for unified analytics, ML and AI workloads that complement AWS-native services – particularly for complex data transformation, MLflow model management and Delta Lake governance. We recommend the right tool for the job, not a single-vendor approach.

How do you ensure data quality across complex pipelines?

We embed automated quality gates throughout every pipeline we build – validating completeness, accuracy and consistency at each stage so data issues are caught before they reach the analytics, reports or AI models that depend on them.

How does your Data Engineering practice connect to your AI capabilities?

We build data platforms with AI in mind from the start – architecting for vector storage, feature engineering and model serving requirements so your data estate is ready when your AI ambitions are, not a barrier to them.

How do you manage data infrastructure costs on AWS and Databricks?

We apply FinOps principles specifically to data and AI workloads – optimising Databricks cluster usage, right-sizing AWS data services and implementing cost allocation frameworks that give you full visibility and control as your platform scales.

Ready to
Transform Your

Infrastructure?

Let’s discuss your data engineering journey. Our experts will help you understand what’s possible and create a tailored roadmap for your organisation.