Principal Data Platform Engineer (Customer Data Architecture)

Customer data architecture — from collection across web and app, through ingestion and modelling, into the activation surfaces where the business acts on it. Set the architectural patterns for client and server-side capture, and the boundary between real-time and batch.

Identity resolution: how users, devices, accounts, and sessions are stitched together across web and app, how that resolution is exposed to downstream consumers, and how it runs in production.

Event schema architecture, versioning, and enforcement mechanisms — contracts, validation, schema registry, CI checks — that turn agreed event definitions into reliable, governed data. Partner with Product Analytics on the taxonomy and naming conventions; you own how the platform makes them real.

Consent architecture end-to-end: how consent is captured, propagated, stored, honoured at activation, and audited across regions and across the full chain from event collection to MarTech, so compliance is enforced architecturally rather than per-implementation.

Establishing quality, lineage, and observability across customer-data flows so issues are detected upstream — not by analysts noticing broken numbers.

Being the technical interface between Data and the functions that depend on customer data: Product Analytics owns what is tracked and why; you own how the platform captures, validates, routes, and governs it. Bridge the same way with MarTech, Engineering, and adjacent systems (CRM, finance, affiliate, partner integrations).

Architecture and operation of warehouse-to-tool data flows: reverse ETL, audience activation, and the contracts that govern data leaving the warehouse. Own the integrity of the data layer that MarTech runs on — consent signals, identity, audience definitions, conversion events.

Data interface between experimentation platforms and the warehouse so assignments, exposures, variant metadata, and feature flag state land cleanly and reproducibly. Define what “experiment-ready data” looks like end-to-end.

Leading the customer-data architecture decisions ahead of us. Frame options, run evaluations, produce decision records, and bring stakeholders to alignment. Bring rigour, not vendor advocacy.

Holding the architectural view of how the warehouse, collection layer, identity resolution, activation tools, and MarTech surfaces fit together as one coherent system. Maintain the technical documentation — architecture diagrams, ADRs, schema registry, integration patterns — that makes the domain knowable to others.

Managing vendors in your domain: hold them to the standards you set internally, drive delivery, escalate quality issues, and evaluate fit as use cases evolve.

Partnering closely with the broader data domain to understand the use cases the customer-data domain needs to support — and designing the architecture that delivers them.