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Data Architect

dmg::mediaLondonPosted 29 Aug 2026

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About this role

Location: DMGT HQ - High Street Kensington, London Position: Permanent About the Role We are seeking an experienced Data Architect to define and govern the data architecture across our technology estate — spanning cloud platforms (AWS and GCP), hosted data centres, and on-premises systems. This role is critical during a period of significant technology transformation: the successful candidate will map, optimise, and design data flows across a complex multi-environment estate; set and enforce data architecture standards; ensure that every project making a material change to our technology or data landscape is optimally designed and documented; and represent those standards at the Technical Design Authority (TDA).

The role sits within the Architecture and Integration team and reports directly to the Enterprise Architect. Main Responsibilities Data Flow Mapping & Optimisation: Produce comprehensive, accurate maps of current-state data flows across the full estate — cloud (AWS, GCP), hosted data centres, and on-premises systems. Identify redundancy, latency, and quality issues in existing flows and define target-state designs that are efficient, resilient, and fit for a cloud-first future.

Data Architecture Standards & Governance: Define, document, and maintain data architecture standards, patterns, and principles for DMG Media. Ensure these standards are consistently applied across all projects and platforms, and act as the authoritative data architecture voice at the Technical Design Authority (TDA). Project Engagement & Design Assurance: Be engaged on all projects that fundamentally change the technology or data estate — from early discovery through to delivery.

Ensure that any new capability, platform, or integration is optimally designed, architecture-reviewed, and documented before build commences. Multi-Environment Architecture: Design and govern data architecture across a hybrid estate that spans AWS, GCP, hosted data centres, and legacy on-premises systems. Ensure data flows, pipelines, and platform designs work coherently across all environments, with clear ownership of cross-environment patterns and data contracts.

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Cloud Platform Design: Lead the data platform architecture across the cloud estate, designing scalable lakehouse patterns, real-time and batch pipeline architectures suitable for AI/ML infrastructure. Person Specification Data architecture expertise across multi-environment estates: proven experience designing and governing data architectures that span cloud, hosted data centre, and on-premises environments simultaneously — not cloud-only.

Multi-cloud platform depth (AWS and GCP): hands-on experience with data services across one or both of AWS (Redshift, Glue, S3 / Lake Formation, MSK, RDS) and GCP (BigQuery, Dataflow, Pub/Sub, Cloud Composer). Data flow mapping and optimisation: demonstrable experience producing current-state data flow documentation, identifying inefficiencies and risks in existing pipelines, and designing optimised target-state flows.

Comfortable working at both the conceptual and technical levels. Data architecture standards and governance: experience defining, documenting, and enforcing data architecture standards, design patterns, and principles at an enterprise level. Prior involvement in architecture governance forums (TDA, ARB, or equivalent) essential. AI and ML platform architecture: experience designing AI/ML data infrastructure — including feature stores, model training pipelines, inference serving, and GenAI / RAG architectures grounded in proprietary content.

Lakehouse and modern data platform design: strong knowledge of open table formats (Apache Iceberg, Delta Lake), zone architecture (landing, cleansed, curated), streaming and batch pipeline patterns, and data quality frameworks. Project engagement and design assurance: able to engage early in the project lifecycle, translate business and technical requirements into architecture decisions, and produce governance-quality documentation (HLDs, LLDs, data flow diagrams, integration designs).

Strong stakeholder management: ability to communicate clearly with both technical teams and senior non-technical stakeholders; able to translate complex data architecture concepts into measurable business outcomes and defensible design decisions. Required Competencies Ability to operate across the full spectrum of architecture detail — from enterprise-level data strategy and standards through to the specifics of pipeline design and platform configuration.

Proactive identification of data architecture risk, technical debt, and optimisation opportunities across the estat

Source listing: greenhouse_dmgmedia

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