DATA ENGINEERING & PIPELINE ARCHITECTURE
Build data pipelines your team can trust in production.
We design and modernise batch, CDC and streaming pipelines with testing, observability, lineage and DataOps built into the operating model — not added after go-live.
Fragile → Reliable and operable
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.
- Pipeline failures are discovered by report users rather than by monitoring.
- Small source changes break downstream jobs unexpectedly.
- Retries, backfills and recovery depend on a few experienced engineers.
- Batch and streaming paths have grown side by side without consistent patterns.
- Release cycles are slow because testing and dependency impact are unclear.
- Nobody can answer whether the data is late, incomplete or simply wrong.
What it costs you
- Fragile pipelines with no contracts, tests or ownership
- Failures stay hidden until someone downstream notices, then recovery is manual
- Downstream data becomes unreliable, and people start keeping their own copies
- Releases slow down while the operational burden rises every quarter
- Trust in analytics and AI falls, because both inherit the same defects
Fragility is rarely one bad pipeline. It is the absence of the things that make change safe: a contract, a test per flow, an alert with an owner on it, and a recovery path someone has actually rehearsed.
What materially changes
| Today | With PaWa |
|---|---|
| One-off pipeline patterns | Reusable engineering patterns and data contracts |
| Manual testing | Automated data and pipeline tests |
| Failures found downstream | Freshness, volume, schema and dependency signals |
| Opaque dependencies | Documented lineage and impact paths |
| Heroic recovery | Runbooks, retries, backfills and named ownership |
| Risky releases | CI/CD, environment controls and deployment discipline |
Today
One-off pipeline patterns
With PaWa
Reusable engineering patterns and data contracts
Today
Manual testing
With PaWa
Automated data and pipeline tests
Today
Failures found downstream
With PaWa
Freshness, volume, schema and dependency signals
Today
Opaque dependencies
With PaWa
Documented lineage and impact paths
Today
Heroic recovery
With PaWa
Runbooks, retries, backfills and named ownership
Today
Risky releases
With PaWa
CI/CD, environment controls and deployment discipline
How we solve it
Mechanisms, not adjectives. Each of these is something we build, document and hand over.
Pipeline architecture
Batch, CDC, micro-batch and streaming chosen by business latency and operating need. Streaming a feed consumed once a day is a cost, not an achievement, and the reverse is a missed obligation.
Data contracts and schema change
An agreement between producer and consumer about shape, semantics and notice — so a column rename becomes a conversation before deployment rather than an incident after it.
Layered transformation patterns
Raw, tested and curated layers with reusable patterns, so a new source follows an established path instead of inventing one per project.
Automated tests and reconciliation
Tests that run on the data, not only the code: row counts, referential expectations, schema stability and reconciliation against source.
Operational observability
Freshness, volume, schema drift, failures and dependency signals with alerts that name an owner. Implemented here; the trust and ownership expectations behind it belong to the Governance & MDM practice.
DataOps
Source control, CI/CD, environment promotion and release discipline. The point is that a change becomes reversible, which is what makes it routine.
Recovery design
Idempotency, retries, backfills, replay and graceful degradation, decided during design rather than improvised at 2am. Most estates can rerun a job; far fewer can rerun it safely twice.
Lineage tied to ownership
Documentation and lineage that connect a flow to the person accountable for it, so impact analysis has somewhere to land.
Reference architecture
Sources feed ingestion — batch, CDC or events — into a raw landing layer where nothing is assumed and everything is retained. Transformation and testing run together, so a defect is caught where it enters rather than found three systems downstream. Tested data becomes curated data products, exposed through a serving, semantic or API layer to analytics, operations and AI. Orchestration, observability, lineage, security and data quality span every stage rather than sitting at the end. Data quality and observability are implemented here; the policy, critical-element definitions and issue-management model behind them are owned by the Governance & MDM practice, which is why they are drawn across the flow rather than as a stage in it.
1Sources
- Operational systems
- SaaS
- Files
- APIs
2Ingestion
- Batch
- CDC
- Events and streaming
3Raw / landing
- Immutable landing
- Retention
- Replay source
4Transform & test
- Business logic
- Data tests
- Contracts
- Reconciliation
5Curated data products
- Modelled datasets
- Conformed dimensions
- Historised data
6Serving
- Semantic layer
- APIs
- Operational feeds
7Consumers
- Analytics
- Operations
- AI and agents
Across the whole flow
Orchestration · Observability · Lineage · Security · Data quality and governance (owned by Governance & MDM)
What you receive
Written artifacts you keep and can act on with any firm, including without us.
- Current-state pipeline and dependency map, with a failure-risk inventory
- Target pipeline architecture and written engineering standards
- Production pipelines within the agreed scope
- Automated tests and reconciliation controls, running in the pipeline rather than beside it
- Monitoring, alerting and service-level expectations with named owners
- CI/CD, environment promotion and deployment pattern
- Recovery and backfill runbooks, plus the ownership model
- Modernisation backlog and knowledge transfer to your engineers
How an engagement runs
- 1
Discover
Inventory what actually runs and who consumes each output. Undocumented jobs feeding something important is the normal finding here, not the exceptional one.
- 2
Design
Agree the ingestion patterns, the contracts, the test strategy and what good alerting looks like for your team's real on-call rota rather than an ideal one.
- 3
Deliver
Rebuild flows in priority order, run them in parallel with what they replace, compare outputs, then decommission the old route as a signed-off step.
- 4
Enable
Hand over runbooks, the DataOps practice and the alerting model, and stay alongside your engineers until they are shipping changes themselves.
Relevant experience
Each item below is labelled with what kind of evidence it is. Nothing here claims a client outcome we cannot support.
Consolidating overlapping integration stacks
A North American insurer running several integration stacks side by side after years of acquisitions, with the same policy data moving between the same two systems by three different routes. Consolidation was sequenced by business risk rather than technical convenience, and decommissioning was treated as a delivery step with its own sign-off rather than as cleanup — which is the usual reason duplicate routes survive their own replacement.
Delivered by our principal in a previous role, before PaWa Data Solutions.
Representative architecture: pipeline modernisation
A pipeline estate where failures are reported by business users rather than monitoring, and every change is priced for the risk of breaking something undocumented. The pattern is inventory first — including the jobs everyone assumes are dead — then contracts, tests and alerting per flow, then recovery design, then decommissioning as a signed step.
What the engagement leaves behind: engineering standards, tests and alerting per flow, recovery runbooks, and a decommissioning list with owners.
Representative engagement — a realistic pattern used to explain our approach, not a client result.
Technology experience
Orchestration
- Airflow
- Dagster
- Azure Data Factory
- Informatica
Transformation
- dbt
- Spark
- SQL frameworks
Streaming and CDC
- Kafka
- Debezium
- Fivetran
- Kinesis
Observability and testing
- dbt tests
- Great Expectations
- Monte Carlo
- OpenLineage
Cloud and platform
- Snowflake
- Databricks
- BigQuery
- Azure Synapse
- PostgreSQL
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
Engineering work is led by our principal, whose fifteen years at Informatica covered more than 300 customer-facing engagements for Tier 1 banks, insurers, telecoms and transport operators. The reliability problems in a pipeline estate are rarely novel, which is the good news.
Full profileQuestions buyers actually ask
Do we need to move to a new platform to get reliability?
Almost never. Reliability comes from contracts, tests, alerting and a deployment practice, and all four can be added to the platform you already run. A migration undertaken to fix reliability usually carries the same absences across to a more expensive home.
How do you decide what to fix first?
By what breaks and what it costs when it does. Flows feeding regulatory and financial reporting get attention first for risk, and the noisiest recurring failures get attention early because they are consuming your team's week.
Can you work with our existing engineering team?
That is the usual arrangement. We often set up the patterns, contracts, tests and DataOps practice while your engineers do the bulk of the rebuild, which is also the fastest route to them owning it afterwards.
What about the pipelines nobody understands any more?
They get inventoried like everything else, then documented, rebuilt or retired. The one thing we will not do is leave a job running because nobody is sure what it does — that uncertainty is the risk, not the job.
Is this the same as data integration work?
Related but not identical, and they are two pages for that reason. Integration is the architecture and patterns between systems; engineering is building and operating the flows reliably. Most engagements touch both, and we will tell you which one your problem actually is.
How does this relate to data quality and governance?
Quality and observability controls are implemented in pipelines, but the policy, the critical data elements and the issue-management model are owned by the Governance & MDM practice. That distinction matters: a quality rule with no business owner is a monitoring job, and monitoring jobs get muted.
What happens to our operating cost?
We will not promise a number before looking. What the work does is make cost and failure patterns visible enough to act on, and retiring genuinely dead jobs usually accounts for more than people expect.
Data Engineering Health Check
A focused review of pipeline reliability, dependencies, testing, observability, recovery and delivery practice. You finish with a failure-risk inventory, engineering standards worth adopting, and a sequenced modernisation backlog.
Scope and commercial terms are agreed in writing before the review starts.