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.

Several years of experience owning customer-data or data-platform architecture in a product-led organisation operating at meaningful scale — including responsibility for the architectural decisions, not just the implementation.

Hands-on experience with first-party data architecture: capture, routing, identity resolution, and the architectural trade-offs across collection paradigms and across the real-time / batch boundary.

Deep technical fluency across the modern data stack: cloud warehouse (BigQuery, Snowflake or equivalent), dbt, reverse ETL (Hightouch or equivalent), event collection (client and server-side), and at least one major activation platform.

Strong working knowledge of the customer data platform ecosystem — packaged, composable, and warehouse-native patterns — and clear views on where each fits.

Hands-on experience with identity resolution in production — deterministic, probabilistic, or both — not only at the conceptual level.

Deep familiarity with consent architecture in practice: how consent signals propagate across server-side capture, Consent Mode, conversion APIs, and reverse ETL, and how to make compliance enforceable at the platform layer rather than per-implementation.

Working knowledge of experimentation platforms and what good experimentation data looks like end-to-end.

A track record of leading architecture decisions across functions — framing options, running evaluations, producing decision records, and bringing stakeholders to alignment.

Experience working alongside Engineering teams as a technical peer — setting specs they implement, reviewing their work, and being trusted to do so.

Strong written communication. Architecture decisions in this role land as documents, ADRs, and specs that other people act on.

A proactive, self-starter mindset. You thrive in ambiguity, work autonomously, and are energised by building in fast-paced, high-growth environments.

Direct experience evaluating, implementing, or migrating between CDP platforms — packaged or composable — with a view on where each pattern actually delivers.

Experience leading a server-side data collection evolution end-to-end.

Experience with data contracts, schema registries, or equivalent governance tooling in production.

Familiarity with mobile analytics platforms and MMPs (Adjust, AppsFlyer or similar), and with ad-platform server-side integrations (Conversion API, Enhanced Conversions, SKAN).

Knowledge of the eSIM, telco, MNO/MVNO, or travel-tech landscape.

Background in B2C or marketplace businesses with non-trivial international, regional, or compliance complexity.

Privacy-first measurement and activation experience in the post-cookie, post-ATT world.