Sr Principal, System Architecture (Digital AdTech)

This role reports to the VP Engineering, Converged AdTech & Linear Systems and leads the architecture for the company's most critical advertising technology platforms. It owns and drives the systems automation architecture of core adtech platforms to improve scalability, reliability, performance, and cost efficiency across the ecosystem. The role is accountable for the architecture, scalability, and operational excellence of systems spanning converged planning, linear and digital advertising, scheduling, and affiliate distribution.

• Define and drive the long-term architecture for AI-powered and agentic systems across large-scale AdTech platforms, including autonomous and human-in-the-loop systems for deployment orchestration, infrastructure lifecycle, and incident response.
• Establish guardrails for safe, observable, and governed AI adoption covering reliability, explainability, and failure handling, integrate AI capabilities into core platform layers such as decisioning, optimisation, experimentation, and operations
• And drive standardisation of agentic workflows across engineering and operations.
• Own the design and evolution of foundational platforms, defining architecture for AdOps automation, optimisation, identity, data, measurement, and activation with clear boundaries and contracts.
• Apply system engineering rigour across reliability, scalability, fault isolation, and performance under extreme load, establish patterns for multi-region, high-availability, and disaster-resilient systems, and drive platform standardisation to reduce fragmentation and improve developer velocity.
• Lead the transformation of fragmented systems into a cohesive, converged AdTech ecosystem spanning linear and digital, identifying systemic bottlenecks across latency, data consistency, and cross-platform dependencies.
• Drive simplification of system topology, eliminate redundant architectures, lead large-scale modernisation from monolith to services and from siloed systems to shared platforms, and ensure seamless interoperability across legacy and next-generation systems.
• Evaluate emerging technologies such as streaming, distributed systems, AI infrastructure, agentic AI, and privacy tech for strategic adoption, and establish architectural governance and decision frameworks.
• Mentor Principal engineers and architects on system design and trade-offs.
• Act as the highest-level technical authority across critical initiatives, reviewing and approving architecture for high-impact, high-risk systems, intervening in complex programmes to de-risk delivery, and aligning engineering, product
• And business stakeholders on critical trade-offs without direct authority.

• 15+ years of experience architecting and scaling enterprise-grade data platforms, with a proven track record in large-scale ingestion, transformation, and activation pipelines.
• Experience automating high-availability, high-throughput production environments, leveraging AI and agentic architectures.
• Advanced knowledge of open data Lakehouse formats (Iceberg, Delta, Hudi), distributed compute frameworks (Spark, Flink, Presto), and streaming technologies (Kafka, Kinesis, Pub/Sub).
• Hands-on expertise in cloud-native data design (AWS, GCP, Azure), microservices, APIs, and serverless architectures, with demonstrated success in modernising legacy platforms into modular, cost-efficient systems.
• Familiarity with identity resolution, audience graphs, and cross-platform measurement systems in media, advertising, or adjacent high-scale transaction domains.
• Deep knowledge of data engineering best practices, CI/CD automation, observability, data lineage, and governance frameworks that ensure reliability and compliance.
• Skilled in cost modelling, benchmarking, and optimising large-scale distributed systems for throughput, latency, and cost efficiency.
• Strong grasp of emerging trends in ad-tech, programmatic ecosystems, ML/AI-driven data products, and privacy-first architectures (GDPR, CCPA, VPPA).
• Experience leading technical evaluations, vendor integrations, and collaborations with third-party data providers, cloud platforms, and open-source communities.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical discipline.