Senior Data Architect

Job description

The Data Technical Architect is the design authority for the data architecture that underpins the Command Centre and enables the Control Tower. The role owns how operational data from source systems across product domains is ingested, integrated, resolved, modelled, certified, and served as a single, trusted, near real‑time view of the business. This is a data‑architecture‑led role. The primary deliverable is the target‑state data architecture – layering, canonical models, event and entity design, the semantic and KPI layer, and the data quality and lineage framework – that allows the Control Tower to detect exceptions, trigger alerts, and support root‑cause analysis with confidence in the underlying data. The role works closely with Command Centre operations, BI delivery, Data Engineering, GDA, Data Platform, source system owners, and Enterprise Architecture, and operates within the standards set by the Tech Governance Lead. The Data Technical Architect is the design authority for the data architecture that underpins the Command Centre and enables the Control Tower. The role owns how operational data from source systems across product domains is ingested, integrated, resolved, modelled, certified, and served as a single, trusted, near real‑time view of the business. This is a data‑architecture‑led role. The primary deliverable is the target‑state data architecture – layering, canonical models, event and entity design, the semantic and KPI layer, and the data quality and lineage framework – that allows the Control Tower to detect exceptions, trigger alerts, and support root‑cause analysis with confidence in the underlying data. The role works closely with Command Centre operations, BI delivery, Data Engineering, GDA, Data Platform, source system owners, and Enterprise Architecture, and operates within the standards set by the Tech Governance Lead.   KEY RESPONSIBILITIES  Data Architecture & Design Own the end‑to‑end data architecture for the Command Centre – how data flows from operational source systems across product domains (e.g. Contracts, Ocean) through to the Control Tower consumption layer. Define the target‑state data architecture and transition roadmap, including the layering model (landing, raw, integrated/curated, consumption) and the storage, compute, and serving choices at each layer. Design the canonical, cross‑domain data layer that gives the Control Tower one consistent representation of the core business objects, independent of the source systems they originate from. Define master and reference data requirements, entity resolution, and cross‑system key mapping so that events from different systems resolve to the same business entity. Establish data domain boundaries, data ownership, and data product definitions aligned to the Command Centre operating model. Produce and maintain core architecture artefacts – conceptual, logical, and physical data models, data flow and lineage diagrams, integration maps, and architecture decision records. Data Integration & Pipeline Architecture Architect real‑time, near real‑time, and batch ingestion patterns – streaming, event‑driven, CDC, API, and scheduled – and define which pattern applies to which source and use case. Define the operational event model and payload standards for the milestones, status changes, and exceptions that drive Control Tower views. Design pipeline patterns for reliability at scale: idempotency, replay, late‑arriving and out‑of‑order events, backfill, and reconciliation back to source. Set standards for orchestration, dependency management, error and dead‑letter handling, and pipeline observability. Define and agree data contracts and SLAs with source system teams, covering schema, frequency, volume, and change management. Define latency, freshness, and throughput targets per data domain and ensure the architecture demonstrably meets them. Data Modelling & Semantic / KPI Layer Design the dimensional, event, and state models required for end‑to‑end milestone tracking, cycle time, dwell, and exception analysis. Architect the semantic and KPI layer so that each metric is defined once, certified, and reused consistently across Control Tower views and downstream reporting. Model for drill‑down, so an alert on the Control Tower can be traced to transaction‑level detail and root cause without leaving the platform. Define the historisation approach – slowly changing dimensions, snapshots, and point‑in‑time reporting – to support trend and retrospective analysis. Design aggregation, pre‑computation, and caching strategies so Control Tower dashboards meet response‑time expectations at production data volumes. Data Quality, Governance & Security Define data quality dimensions, rules, and thresholds at each architectural layer, and design how failures are detected, quarantined, and escalated. Ensure Control Tower alerting is built on trusted data – design the freshness, completeness, and validity checks that gate what is surfaced to operations. Design end‑to‑end metadata and lineage capture, from source system field through to published KPI. Design role‑based access, row and column level security, masking, retention, and audit into the data architecture rather than bolting it on later. Ensure alignment with enterprise data architecture, cloud strategy, and BI governance standards, and work with the Tech Governance Lead on dataset certification. Identify, document, and drive remediation of data architecture technical debt. Delivery & Execution Translate the architecture into sprint‑ready designs, data contracts, and backlog items for data engineering and BI delivery t