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Engineering · Data Engineering
Make business data clean, connected and usable.
We build the pipelines, integrations, databases, warehouses and reporting-ready models that support trusted reporting, operational workflows, software and AI.
- Built to a validated problem
- Governed by default
- Owned after handover
Trusted by established businesses
The constraint
The problem this solves
If several of these sound familiar, the constraint is usually infrastructure, not effort.
Data trapped in disconnected systems
Important information lives in your CRM, ecommerce platform, accounting software and ad accounts, and none of them talk to each other.
Reporting is manual and slow
Someone exports, copies and pastes every week. Reports arrive late, depend on one person, and break when that person is away.
Systems produce conflicting numbers
Sales says one figure, finance says another. Nobody can explain the gap, so nobody fully trusts either.
Records are duplicated or incomplete
Duplicate customers, missing fields and outdated entries make every report a judgement call rather than a fact.
Dashboards nobody trusts
Dashboards exist, but the source data underneath is inconsistent or incomplete, so decisions still fall back to instinct.
Tracking gaps across the funnel
Websites, forms, campaigns and CRMs record activity inconsistently, so you cannot follow a customer journey from first click to revenue.
Data quality blocking AI and automation
AI, automation and business intelligence initiatives keep stalling because the underlying data is not clean, connected or accessible enough to build on.
Every week this continues, staff hours go into manual exports and reconciliation, integrations quietly drift out of sync, and the reporting, AI and automation initiatives that depend on clean, connected data stay stuck.
Capabilities
What sits inside
Decisions
Start with the use case
Data infrastructure justified by a specific use earns its keep; a platform built "because data" rarely does. These are the use cases we scope against. If yours isn't defined yet, Data & Decision Making is the right first conversation.
Reporting
Trusted reporting
Leadership needs numbers that agree: one governed source feeding dashboards and reviews instead of manual exports.
Operations
Operational integration
Orders, jobs and records need to move between systems automatically, without re-keying at every handover.
Customers
A single customer view
Sales, service and finance need to see the same customer once: deduplicated, complete and current.
AI
AI knowledge and data
An AI initiative needs clean, connected, accessible data before it can answer or automate anything reliably.
Software
An application backend
A custom application or portal needs a data layer designed to carry it, not a spreadsheet behind an interface.
Tracking
Tracking and attribution
Marketing decisions need customer journeys captured consistently from first touch to revenue.
Deliverables
What you receive
- ✓Data audit report
- ✓Data source inventory
- ✓Current-state data flow map
- ✓Data quality findings and recommendations
- ✓Technical data architecture plan
- ✓Data engineering roadmap
- ✓Database or data warehouse setup
- ✓Data pipelines and API integrations
- ✓Cleaned and transformed datasets
- ✓Reporting-ready data models
- ✓Source-of-truth metric definitions
- ✓Tracking improvements
- ✓Data dictionary and integration documentation
- ✓Testing and validation report, plus handover documentation and training notes
How it runs
How we approach the work
Data infrastructure fails when it is built before the problem is understood, so we diagnose first.
- 01
Audit what exists
We inventory your data sources, map how data currently flows between systems and document where quality breaks down, before proposing anything.
- 02
Anchor to the business need
We work from the reporting, automation and decisions the business actually needs (often already defined in consulting work), so infrastructure is built for a purpose, not for its own sake.
- 03
Design the architecture
A technical data architecture plan and sequenced roadmap: what to connect, where data lives, how it moves, and what order to build in, fit for purpose, traceable, documented and cost-aware rather than gold-plated.
- 04
Build and connect
Pipelines, integrations, warehouse and data models built incrementally, with each stage tested against real business data rather than assumptions.
- 05
Validate and document
Testing and validation against known figures, plus a data dictionary and integration documentation, so the system is understood, not just installed.
- 06
Hand over or manage
Handover documentation and training notes for your team, with the option of ongoing pipeline monitoring under Technology Managed Services.
Outcomes
The commercial outcomes
Efficiency
Less manual reporting work
Pipelines replace exports and copy-paste, typically reducing the staff hours spent preparing routine reports.
Confidence
Numbers the business can trust
Agreed metric definitions and validated pipelines typically end the debate over whose figure is right.
Visibility
Faster access to key metrics
Marketing, sales, customer, operational and financial data in one place, giving leadership clearer and faster visibility.
Risk
Reduced key-person dependency
Reporting stops depending on the one person who knows the spreadsheet, reducing single points of failure and the risk of reporting errors.
Foundations
A stronger base for what's next
Dashboards, automation and AI initiatives typically move faster when the data beneath them is clean, connected and documented.
Scalability
Decision infrastructure that scales
As the business grows, the data systems are designed to grow with it, rather than multiplying spreadsheets and manual workarounds.
Scope
What this is, and what it isn't
What's included
- Data pipelines and API integrations between your business systems
- Data warehouse, database and reporting-ready data model builds
- Data cleaning, transformation, validation and quality improvement
- Source-of-truth metric definitions implemented in the data layer
- Tracking improvements across websites, forms, campaigns and CRMs
- Data audit, source inventory and current-state data flow mapping
- Technical data architecture plans and a sequenced engineering roadmap
- Documentation: data dictionary, integration docs, handover and training notes
What this is not
- Basic spreadsheet tidying or manual data entry
- Unbounded data cleansing with no defined use case behind it
- KPI design and dashboard strategy: that is Data & Decision Making
- Data science, machine learning or AI model development by default
- Legal, privacy, compliance, cybersecurity or financial advice
- Full CRM, ERP or legacy-system replacement unless separately scoped
- One-off dashboards built without a reliable data foundation underneath
- Ongoing maintenance, unless covered by a support or managed service agreement
Who it's for
The technical foundations behind reliable reporting
Data Engineering is a technical implementation service that builds the data foundations behind better reporting, analytics, automation, AI and custom software. It covers how data is collected, moved, stored, cleaned, validated and made available for practical use: pipelines, API integrations, warehouses, reporting databases, data models, tracking and source-of-truth metric definitions. It usually follows the consulting work that defines what should be measured.
Discuss your data foundation- Data spread across disconnected systems
- Reliant on manual, copy-paste reporting
- Don't trust your dashboards
- Need your business data connected
- Preparing for AI or automation
- Need a single source of truth
- Legacy systems needing modern data access
Probably not the right fit
- −You only want cosmetic changes to an existing dashboard
- −You're unable or unwilling to provide access to the systems that hold your data
- −You need regulated-data, privacy or compliance advice, which sits outside our scope
- −You want a one-off tactical fix with no interest in the underlying foundations
- −A well-kept spreadsheet still genuinely covers your reporting needs
What this unlocks
Data Engineering is rarely the destination.
Clean, connected data is what makes reporting, AI, portals and managed systems work. Select a pathway to see what Data Engineering prepares before it.
Data Engineeringthe foundation layer
Prepares: pipelines · integrations · warehouse · data models · validation · reporting-ready datasets.
Data EngineeringData & Decision Making
When it comes into play
When the use case, KPIs or reporting requirements are not yet defined.
What it owns
Defines what to measure, the KPI framework and the decision rhythm.
Relationship to Data Engineering
The use-case and reporting requirements the foundation is scoped against.
Proof
Selected work.
Payment Processing Cost Reduction. An ecommerce retailer was losing a significant percentage of revenue to payment processing and invoice platform fees. Web Lifter redesigned the entire sales and payment workflow, replacing Stripe and Paycove with a direct Westpac PayWay integration and a custom-built invoicing platform. The new architecture reduced transaction costs, streamlined operations, and delivered immediate profit improvements without requiring any increase in sales volume.
Read the caseWhat clients say
“We can't recommend Web Lifter highly enough … a digital partner who could understand our operations, connect the dots between marketing and backend systems, and deliver real results.”
Questions
Common questions
Fit
It builds the technical systems that collect, connect, clean, transform, validate and make business data usable: the foundation beneath reporting, automation, AI and software.
Established businesses with important data scattered across CRMs, ecommerce, ads, finance, operations, spreadsheets or legacy systems, where reporting is unreliable and the cause is unclear.
Usually yes, if the data is scattered, inconsistent or manually prepared. A dashboard built on unreliable data will still be unreliable.
Scope
Data & Decision Making defines what should be measured and how decisions get made. Data Engineering builds the infrastructure that makes those reports and dashboards reliable.
Data Engineering prepares clean, connected, accessible data. AI Engineering builds the assistants, automations and features that rely on it.
It can reduce dependence on manual spreadsheets by building cleaner pipelines, databases and reporting-ready datasets. It is not basic spreadsheet tidying.
Yes, where scoped: identifying duplicates, incomplete records, inconsistent fields, broken tracking and transformation needs. It is not unbounded cleansing: the effort is anchored to the use case the data must serve.
Not under this service. Data Engineering connects and extracts value from the systems you already run. Full CRM, ERP or legacy-system replacement is a separate decision, separately scoped, usually informed by Technology Strategy rather than assumed at the outset.
Access
Commonly CRMs, ecommerce platforms, ad accounts, finance systems, websites, analytics tools, forms, operational systems, spreadsheets and legacy databases.
It depends on the systems involved: usually source systems, APIs, databases, reporting tools, tracking platforms and relevant documentation.
Start with a data audit, data source inventory and current-state data flow map. That's exactly what the first phase produces.
Dashboards, AI & Software
Data Engineering prepares the data foundation for dashboards. Dashboard strategy and the reporting layer sit with Data & Decision Making or Kepler.
If the AI use case depends on business data, documents or reporting logic, Data Engineering is often needed first to make that data reliable and accessible. AI Engineering then builds on the foundation.
Yes. It can prepare the data layer that feeds Software Development projects, Company Portal builds or operational dashboards.
It can be a scoped implementation project. Ongoing monitoring, maintenance and improvement move into Technology Managed Services if needed.
Deliverable
Reliable data infrastructure: pipelines, integrations, data models, validated datasets, documentation and a foundation that supports reporting, AI, automation and software.
Commercial
Scope varies with the number of systems involved, the state of the data inside them and the infrastructure required, so there is no standard price. Pricing is confirmed at scoping, usually once a data audit has established what actually needs building.
The next step