Data Architect – Minerva SA Reg Risking
Clearance Required: SC
Duration: 6 months
Location: Telford with 2 days/week in office
IR35 Status: Mandated PAYE only
Project Overview
* The Preventative Risking (PR) team within RIS is responsible for managing the risking and compliance referral processes for Self‑Assessment (SA) registrations.
* Currently, the system identifies approximately 200,000 fraudulent registrations out of 1 million, resulting in an estimated £51 million to £219 million in lost SA repayment claims for HMRC (based on 2021/2022 figures).
* To address this, a proof of concept (POC) was developed using SAS Enterprise Guide for table creation, SAS Studio V and SAS RTENG to build the SA registration network, and SAS Viya 3.5 tools for risk assessment of new SA registrations.
* The POC leveraged data from 20 different sources, most of which were already housed in the Minerva Oracle database. Previously, some data had to be transferred manually. However, the automated file transfers described in this Solution Design Document (SDD) will now move that data to the SAS platform using approved Enterprise Architecture (EA) integration patterns, with the initial phase targeted for delivery in April 2026.
* This role will form part of a new scrum team within Minerva Platform to develop and deliver the Ingestion and Risking within the SAS Platform including IDP.
Data Architect Responsibilities
A data architect designs and builds data models to fulfil the strategic data needs of the organisation, as defined by chief data architects.
* Design, support and provide guidance for the upgrade, management, decommission and archive of data in compliance with data policy.
* Provide input into data dictionaries.
* Define and maintain the data technology architecture, including metadata, integration and Business Intelligence or data warehouse architecture.
* Communicate between the technical and non‑technical stakeholders.
Skill and Experience Levels
* Level: Working – Communicate effectively with technical and non‑technical stakeholders; support and host discussions within a multidisciplinary team; advocate for the team externally and manage differing perspectives.
* Level: Working – Undertake data profiling and source system analysis; present clear insights to colleagues to support the end use of the data.
* Level: Working – Understand and assure data governance; make recommendations to ensure compliance.
* Level: Working – Explain concepts and principles of data modelling; produce, maintain and update data models; reverse‑engineer data models from a live system.
* Level: Working – Develop data standards; analyse and undertake impact analysis of breaches.
* Level: Working – Work with metadata repositories to complete integration impact analysis; maintain accurate repositories.
* Level: Working – Initiate and monitor actions to investigate patterns and trends; consult specialists; determine preventative measures.
* Level: Awareness – Explain strategic context; support strategic planning; turn business problems into data design.
* Level: Awareness – Show awareness of opportunities for innovation with new tools and uses of data.
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