DATA INTEGRATION & ENGINEERING
Your systems are connected. Your data should be too.
ERP, CRM, finance, SaaS and operational systems each hold part of the truth. We design the governed integration architecture that turns those fragments into data your business can rely on, and hand it over documented.
Fragmented → Connected
Does this sound familiar?
If several of these are already true, this is the right conversation. If none of them are, it probably is not.
- Reports need manual reconciliation before anyone will trust them.
- Two systems disagree about the same customer, product or transaction.
- A new integration takes months to design, and nobody can say why.
- Pipeline failures are reported by business users, not by monitoring.
- Critical mappings and dependencies live in one person's head.
- Analytics and AI teams spend more time finding and fixing data than using it.
What it costs you
- Systems hold overlapping, conflicting versions of the same entity
- Someone reconciles them by hand before every reporting cycle
- Reporting is late, and the numbers are argued about rather than used
- Decisions get made on the version someone trusts most
- AI and analytics inherit every defect, at speed and at scale
The cost is rarely the integration tooling. It is that no one can say which pipeline is authoritative, so every question about a number becomes an archaeology exercise and every change is priced for the risk of breaking something invisible.
What materially changes
| Today | With PaWa |
|---|---|
| Point-to-point integrations | Governed integration patterns |
| Fragile pipelines | Observable data flows |
| Manual reconciliation | Automated validation and quality controls |
| Conflicting entities and definitions | Consistent business entities and mappings |
| Tribal knowledge | Documented architecture and lineage |
| Consultant dependency | A capability your team owns |
Today
Point-to-point integrations
With PaWa
Governed integration patterns
Today
Fragile pipelines
With PaWa
Observable data flows
Today
Manual reconciliation
With PaWa
Automated validation and quality controls
Today
Conflicting entities and definitions
With PaWa
Consistent business entities and mappings
Today
Tribal knowledge
With PaWa
Documented architecture and lineage
Today
Consultant dependency
With PaWa
A capability your team owns
How we solve it
Mechanisms, not adjectives. Each of these is something we build, document and hand over.
Integration architecture
The patterns connecting systems, platforms and consumers: boundaries, latency expectations and who owns each interface. Decided before tooling, because the tool follows the pattern and not the reverse.
Data engineering
Batch, change data capture, API and streaming pipelines chosen per use case. Streaming a feed that is consumed once a day is a cost, not an achievement.
Data quality
Validation at ingestion and transformation so defects are caught where they enter, rather than found by a business user three systems downstream.
Observability
Freshness, volume, schema drift, failures and anomalies, with alerts that name an owner. If your users detect incidents before your monitoring does, that is the gap.
Governance in the flow
Ownership, lineage, classification and access built into the pipeline rather than documented beside it. Controls that live in a separate document stop being true within a quarter.
DataOps
Repeatable build, test, deploy and operate practice, so a change to a pipeline is a routine release instead of an event.
Reference architecture
Source systems feed a governed ingestion layer, where quality controls run before anything is trusted downstream. Transformed data lands in a governed platform, is exposed as semantic models and data products, and is consumed by analytics, operations, AI and applications. Security, lineage, observability and governance are not a stage in that flow; they span all of it, which is why they are drawn across the bottom rather than as a box in the middle.
1Sources
- ERP
- CRM
- SaaS
- Files
- APIs
- Operational DBs
2Ingestion
- Batch
- Change data capture
- APIs
- Streaming
3Quality & transform
- Validation rules
- Reconciliation
- Business logic
4Governed platform
- Curated storage
- Mastered entities
- Reference data
5Data products
- Semantic models
- Interfaces
- Events
6Consumers
- Analytics
- Operations
- AI & agents
- Applications
Across the whole flow
Governance & ownership · Lineage · Data quality monitoring · Observability · Security & access control
What you receive
Written artifacts you keep and can act on with any firm, including without us.
- Current-state integration map, with the risks and the undocumented jobs named
- Target integration architecture and the decisions behind it, including what was rejected
- Production pipelines and interfaces within the agreed scope
- Data quality controls and reconciliation rules
- Lineage and dependency documentation
- Monitoring, alerting and operational runbooks
- A delivery backlog and modernisation roadmap, sequenced by risk
- Knowledge transfer to your team, treated as a deliverable rather than a final meeting
How an engagement runs
- 1
Discover
Inventory what actually runs, including the jobs everyone assumes are dead, and identify who consumes each output.
- 2
Design
Agree the target pattern and the sequence. Flows feeding regulatory and financial reporting move last, once the pattern is proven on lower-stakes data.
- 3
Deliver
Build in scope, run in parallel with what it replaces, compare outputs, then decommission the old route as a signed-off step.
- 4
Enable
Hand over documentation, runbooks and the operating practice, and work alongside your team until they are running it.
Relevant experience
Each item below is labelled with what kind of evidence it is. Nothing here claims a client outcome we cannot support.
Entity resolution across retail, commercial and wealth banking
A Tier 1 North American bank whose lines of business each held their own version of a customer, leaving financial crime investigators to reconcile them by hand. The work began with profiling why records failed to match — name conventions across languages, addresses to different standards, identifiers in fields never meant to hold them — then tuned match rules against cases the investigations team already knew the answer to.
Delivered by our principal in a previous role, before PaWa Data Solutions.
Representative pattern: consolidating overlapping integration stacks
Years of acquisitions leave several integration stacks running side by side, with the same data moving between the same two systems by three different routes. The work is inventory first, then consolidation sequenced by business risk rather than technical convenience.
What the engagement leaves behind: one documented ingestion pattern, parallel-run evidence per migrated flow, and decommissioning treated as a delivery step with its own sign-off.
Representative engagement — a realistic pattern used to explain our approach, not a client result.
Technology experience
Integration & ETL
- Informatica
- dbt
- Airflow
- Fivetran
- Kafka
- Debezium
Platforms
- Snowflake
- Databricks
- BigQuery
- Azure Synapse
- PostgreSQL
- SQL Server
Governance & quality
- Collibra
- Informatica Axon & DQ
- Great Expectations
- OpenLineage
Cloud
- Azure
- AWS
- Google Cloud
Platforms and tools we have worked with directly. This is experience, not a partnership claim: PaWa Data Solutions holds no reseller agreement or partner status with any vendor listed here, which is what keeps the recommendation neutral.
Papa S. Nguer
VP of Technical Sales & Engineering, PaWa Data Solutions
Integration work is led by our principal, who spent fifteen years at Informatica and ran more than 300 customer-facing proving engagements for Tier 1 banks, insurers, telecoms and transport operators. The person who scopes your engagement is the person who delivers it.
Full profileQuestions buyers actually ask
Do we need to replace our current integration platform?
Usually not. Most estates have a viable platform used badly rather than the wrong platform. We assess what you have against what you need before recommending any change, and replatforming is the recommendation only when the pattern genuinely cannot be built on what is there.
Can you work alongside our internal engineering team or existing SI?
Yes, and it is the common case. We are frequently brought in for architecture and governance while an internal team or incumbent integrator does the build. We will say plainly where responsibilities need to divide to avoid two teams owning the same interface.
Do you recommend particular vendors?
We have no reseller margin and no partner quota, which is deliberate. Our principal spent fifteen years selling one vendor's platform, so the recommendation comes with an unusually direct view of how these tools get positioned, and it is not shaped by what we would earn from it.
Can we modernise incrementally rather than replatform everything?
That is the default. Consolidation is sequenced by business risk: prove the pattern on lower-stakes data, then move the flows feeding regulatory and financial reporting last. A big-bang cutover concentrates all the risk at the moment you can least afford it.
How do you handle governance and security during the work?
An NDA is signed before any access. We work read-only where possible, and governance is built into the flow rather than added afterwards: ownership, lineage, classification and access controls are part of the pipeline design, not a document written beside it.
What happens after go-live?
You get runbooks, monitoring and alerting that names owners, and documented lineage. Knowledge transfer is a deliverable with time allocated to it, not a final presentation. The measure is whether your team can change what we built without us.
How is the Integration Architecture Review different from the Data Health Check?
The Data Health Check looks across the whole estate — architecture, integration, quality, governance and operating risk — in two to three weeks. The Architecture Review goes deep on the integration estate specifically, in one to two weeks, and suits a team that already knows integration is the problem.
Integration Architecture Review
A focused one to two week review for teams with a brittle, expensive or hard-to-explain integration estate. You finish with a current-state map, the risks named, and a target architecture sequenced by business risk.
Scope and commercial terms are agreed in writing before the review starts.