Practice

Data Engineering

Pipelines, lakehouses and warehouses built by engineers who understand what a policy file, a treaty, a reserve cell, a credit exposure and a reporting basis actually are.

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Data Engineering Pipeline nodes. Source systems on the left flow through transformation stages into BI-ready data products on the right. One node carries an accent marker for data-quality controls. policy claim treaty transform + controls actuarial finance risk & BI DOMAIN-AWARE PIPELINES — POLICY, CLAIM, TREATY Data Engineering Pipeline nodes. Source systems on the left flow through transformation stages into BI-ready data products on the right. One node carries an accent marker for data-quality controls. policy claim treaty transform + controls actuarial finance risk & BI DOMAIN-AWARE PIPELINES — POLICY, CLAIM, TREATY

Practice signals 1 / 3

Source systems remain visible

The pipeline should show where client data starts and how it is shaped for actuarial and BI use.

Who this is for

CIOs, CDOs, heads of data, enterprise architects and analytics leaders at insurers, banks and investment managers.

Where we help

Most financial-services data platforms are built by engineers who do not know the business, then handed to actuarial, risk and finance teams who do not know the platform. The result is a data layer that is technically correct and operationally useless. Reports are still rebuilt in Excel. Reconciliations are still manual. AI projects stall on data quality.

What we do

  • Design and build cloud and on-premise data platforms — lakehouse, warehouse, event-driven or hybrid.
  • Engineer pipelines that are aware of insurance, banking and investments domain shapes (policy, claim, treaty, exposure, reserve, transaction).
  • Implement data quality, lineage, observability and access controls as core, not as an afterthought.
  • Modernise legacy ETL into versioned, testable, observable code.
  • Land data into BI-ready models that finance, actuarial and risk can actually use.
  • Integrate cleanly with the actuarial engines, data platforms, BI tools and cloud stacks you already operate.
Source-to-BI pipeline with quality gates A horizontal pipeline diagram. Domain source systems on the left feed through ingest, cleansing, modelling and BI-ready stages, with explicit data-quality gates and lineage between each. One gate is highlighted in accent red as the point where governance and lineage are introduced. DOMAIN-AWARE PIPELINE — POLICY · CLAIM · TREATY · EXPOSURE Source systems → BI-ready, with quality gates 1 · SOURCE Policy Claim Treaty Exposure core admin · CRM · external feeds 2 · INGEST CDC + batch schema-on-read contract tests versioned DQ QUALITY GATE 3 · MODEL Domain shapes policy graph reserve cells treaty layers 4 · STORE Lakehouse cloud data platform or warehouse cloud or on-prem 5 · BI-READY Decision-ready BI & reporting tools actuarial · risk · finance via API or warehouse LINEAGE Every number is auditable end-to-end: any cell on a finance dashboard can be traced through the lakehouse, modelling step, ingest pipeline and source system that produced it. Source-to-BI pipeline with quality gates A horizontal pipeline diagram. Domain source systems on the left feed through ingest, cleansing, modelling and BI-ready stages, with explicit data-quality gates and lineage between each. One gate is highlighted in accent red as the point where governance and lineage are introduced. DOMAIN-AWARE PIPELINE — POLICY · CLAIM · TREATY · EXPOSURE Source systems → BI-ready, with quality gates 1 · SOURCE Policy Claim Treaty Exposure core admin · CRM · external feeds 2 · INGEST CDC + batch schema-on-read contract tests versioned DQ QUALITY GATE 3 · MODEL Domain shapes policy graph reserve cells treaty layers 4 · STORE Lakehouse cloud data platform or warehouse cloud or on-prem 5 · BI-READY Decision-ready BI & reporting tools actuarial · risk · finance via API or warehouse LINEAGE Every number is auditable end-to-end: any cell on a finance dashboard can be traced through the lakehouse, modelling step, ingest pipeline and source system that produced it.

Diagram signals 1 / 3

Source systems remain visible

The pipeline should show where client data starts and how it is shaped for actuarial and BI use. The body diagram should make this route explicit enough to discuss in a working session.

Domain-aware pipeline with quality gates and end-to-end lineage.

Outcomes

  • Pipelines that can be reasoned about and audited.
  • Less time spent on data wrangling, more on analysis.
  • A foundation that AI and analytics work can actually be built on.
  • Reduced operating cost of the data estate.

Engagement model

We can run as a focused build team, embed alongside an internal team, or assess an existing estate and produce a prioritised remediation plan.

Related software

Symphony AutomateOpen Architecture and Deployment

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