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WPP Media

AI-native global media services network

Lead Architect - Data & AI Platforms

Data EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Proven cloud platform and data engineering track record, with deep Microsoft Azure expertise and hands-on exposure to AWS and/or Google Cloud Platform.
  • Strong Terraform proficiency using reusable modules, testing, and modular code structures.
  • Hands-on experience with Azure Data Factory, Synapse Analytics, and Azure Databricks (Spark).
  • Strong continuous integration and continuous delivery experience using GitHub Actions or Azure DevOps for infrastructure-as-code and data workloads.
  • Solid coding capability in Python and SQL, including Spark/Databricks performance tuning and data modelling for analytical and AI workloads.
  • Ability to discuss data platform code, Terraform modules, or pipelines personally authored, including design decisions.
  • Examples of reference architectures, reusable modules, or accelerators designed, packaged, and adopted by teams beyond the candidate's own.
  • Demonstrated ability to consult with senior client and agency stakeholders, including non-technical audiences, present pragmatic technical solutions, and independently lead architecture workshops.
  • Ability to implement virtual networks, private endpoints, managed identities, Key Vault, role-based access control, and governance patterns as Terraform rather than through portal configuration or change requests.
  • Ability to facilitate client workshops, write concise design documents and runbooks, align cross-functional teams, and mentor engineers.
  • Ability to translate commercial objectives into technical roadmaps and explain trade-offs to non-technical stakeholders.
  • Ability to design for operability, security, and cost across Azure and other cloud platforms.
  • Ability to measure outcomes using deployment frequency, lead time for changes, data pipeline service-level agreements, cost efficiency, and platform reliability metrics.
  • Must have the right to work in the United Kingdom.

Responsibilities

  • Architect and implement Azure-first client data platforms, including landing zones, networking, identity, and data platform services such as Azure Data Lake Storage, Synapse, and Databricks, extending patterns to AWS or Google Cloud Platform when required.
  • Design and implement data ingress patterns across platforms, focusing on Azure Data Factory pipelines and data flows for ingestion, transformation, and orchestration; establish parameterization, retry, and error-handling patterns to meet pipeline service-level agreements, then work with the Data Engineering team to scale them into production.
  • Deliver infrastructure-as-code using Terraform, create reusable modules and policy-as-code guardrails, and establish repository strategies, branching, pull requests, multi-stage pipelines, approvals, and automated promotion gates.
  • Package proven designs as deployable assets, including Terraform modules, pipeline templates, reference implementations, and accelerators, with documentation and worked examples for reuse across clients.
  • Introduce DataOps practices such as automated unit and integration testing and data quality checks, and build AI capabilities including vector search, retrieval-augmented generation workflows, and agentic capabilities.
  • Build working prototypes of data and AI concepts, including retrieval-augmented generation, agentic workflows, streaming, and real-time patterns, and demonstrate them to clients to shape demand, test feasibility, and reduce delivery risk.
  • Own the technical relationship with client and agency stakeholders; run discovery and design workshops, present at design authorities, and translate commercial objectives into phased roadmaps, costs, and trade-offs for non-technical audiences.
  • Codify private endpoints, managed identities, Key Vault-backed secrets, role-based access control, network isolation, data encryption, and Purview cataloging and lineage as reusable modules.
  • Optimize compute and storage across Spark clusters, autoscaling, partitioning, and serverless options; track, report, and drive cloud cost savings.
  • Build pipeline and platform monitoring using Azure Monitor and Log Analytics, and define service-level objectives and alerting for the owning engineering team.
  • Lead streaming and micro-batch patterns, including Event Hubs, Kafka on Azure, and Structured Streaming, where required by client use cases.
  • Mentor engineers, review pull requests and infrastructure-as-code, and hand over reusable patterns and accelerators to the Data Engineering team.

Desired Qualifications

  • Production experience shipping retrieval-augmented generation or agentic AI workloads to live users, including vector databases and large language model orchestration frameworks.
  • Experience with event-driven architectures using Event Hubs, Kafka, Stream Analytics, or Functions, and change data capture patterns.
  • Experience with lakehouse and Delta patterns on Databricks, and awareness of MLflow and feature stores.
  • Experience with data quality tooling and frameworks such as Great Expectations and Deequ, and dbt for transformation governance.
  • Experience tracking data lineage, defining data quality policies and procedures, and enforcing them at runtime.
  • Experience integrating API Management, Logic Apps, or Azure Functions.
  • Experience with Azure Enterprise-Scale Landing Zones and Azure Policy.
  • Microsoft Certified Azure Solutions Architect Expert, Azure Data Engineer Associate, or equivalent AWS or Google Cloud Platform certifications.

About the Company

WPP Media

AI-native global media services network

WPP Media is a global media services arm that manages more than $60 billion in annual advertising investments for leading brands. It operates across a network of established agencies (Mindshare, Wavemaker, EssenceMediacom) and combines media planning, data, production, and commerce through its proprietary WPP Open platform. Its Open Intelligence data model uses real-time behavioral signals to drive predictive, faster, and smarter marketing campaigns. The company positions itself as an AI-native media collective, consolidating capabilities under a single technology stack to improve efficiency and scale for advertisers. Its goal is to help clients buy and optimize media more effectively by leveraging integrated data, advanced analytics, and automated execution at scale.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
2003

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