Data Platform Services

A lot of consultancies will help you decide what to build. Fewer will actually build it, and fewer still will deliver it in a state your team can own and operate without ongoing dependency on the people who built it.

We design and deliver a working data platform, structured from the start so that once the bulk of the work is done, you don’t need a large team to keep it running. The engagement scales to fit the work: heavy during the build, leaner once it is live.

Architecture design

We turn strategy into an architecture your team can build to. High-level design covers the overall structure: layers, components, data flows, access model. Low-level design goes into the specifics: schema definitions, ingestion patterns, transformation logic, security controls. Both are documented, version-controlled, and yours.

Platform build

We build it. Ingestion pipelines, Bronze-Silver-Gold medallion layers, data contracts, governance controls, catalogue, persona-based access. On AWS native, Databricks or a combination, and in your own cloud account when your data must stay within your environment.

Source onboarding, ETL and curation

Each source system requires its own ingestion pipeline, transformation logic, and curation into the governed layers. The time this takes depends on the source: a clean, well-documented database is quick, while a legacy system with inconsistent schemas takes longer.

We onboard sources end to end, from raw ingestion through to curated, consumption-ready datasets. We can own the full source estate until every feed is live, or onboard the first set and train your team to handle the rest. Before committing to a timeline, we run source discovery and light profiling, assessing each feed for volume, consistency and complexity, to give you a per-source effort estimate before the build begins.

Delivery within your framework

We slot into whatever your organisation already runs: Scrum, Kanban or SAFe. Deliverables are mapped to tickets in your tooling, progress is visible in your sprints, and nothing happens invisibly. A budget holder can point to exactly what was delivered in each phase.

Documentation and handover

Every engagement closes with a full handover pack: architecture diagrams, Architecture Decision Records explaining why key choices were made, runbooks for the operations team, and a platform handbook covering day-to-day ownership.

Why professional services makes financial sense

A data platform build is intensive and front-loaded: design, ingestion, governance, testing. Once that work is done, the platform runs on a fraction of the resource it took to build. Hiring permanent staff to deliver the build means carrying headcount that has little to do once it’s live. Professional services gives you the right level of expertise for the duration of the work, without the long-term overhead.

ROI is the measure

Every engagement is structured around return on investment in terms a CFO can read: what data is now available, what decisions can be made faster, what manual processes have been eliminated, what compliance risk has been removed. The deliverables are the evidence: designs, decision records, code, tickets and handbooks. A paper trail of exactly what was built, why, and what it cost.

What a typical engagement looks like

Phase 1: Design. High-level and low-level architecture. Data source assessment. Access model. Delivery plan.

Phase 2: Build. Platform implementation. Source onboarding. Ingestion pipelines. Governance layer. Testing and validation.

Phase 3: Handover. Documentation. Runbooks. Knowledge transfer. Operational sign-off.

After Phase 3 the platform is yours: fully documented, operationally stable and sized to run lean. DataPhoenix remains available for ongoing support or follow-on phases, and the heavy lifting is done.

Each source is onboarded in stages. First contact is a light intake with the data owner, the technical owner and the consumers, and the owner commits to notify the platform of material changes, a commitment that is re-confirmed on a regular cycle. A second stage records the business context, the freshness the consumers need, the data classification and a scope decision: what is structured now for the committed use case, what is landed as supplied and for how long, and what is excluded. A third stage applies only to sources with real complexity, such as several endpoints or mixed authentication.

Nothing reaches a consumer until the readiness gates are met: classification, a linked contract for the refined and curated layers, and catalogue entries on every layer. Quality checks run as part of the same delivery, as described in data quality and anomaly detection are different jobs. DataPhoenix builds AI into every part of delivery and uses it to extend what an existing platform can do.