Vishal Anand

• Led strategic integrations between Amazon Marketing Cloud (AMC) and enterprise Customer Data Platforms and clean room providers (LiveRamp, Infosum, Salesforce, AppsFlyer), enabling secure first-party data collaboration, attribution analysis, and unified customer insights across marketing ecosystems.

• Advise enterprise brands and agency partners on customer data architecture, identity resolution frameworks, and mobile attribution measurement strategies, enabling scalable audience activation and performance analysis.

• Drive end-to-end customer data platform implementations including technical discovery, solution architecture, proofs-of-concept, and onboarding, partnering with Sales, Product, and ecosystem partners.

• Act as the primary technical advisor for enterprise customers, diagnosing complex data ingestion, attribution, and analytics integration challenges across marketing data environments.

• Enable customer engineering and marketing teams through architecture workshops, platform deep-dives, and best practice guidance, accelerating adoption of data-driven marketing and attribution capabilities.

• Delivered measurable business impact including increasing first-party data match rates to 50–60% for Renault and Peet’s Coffee, and deploying predictive audience strategies for EDF reducing CPM by 17% while improving campaign efficiency.

• Led architecture and delivery of an enterprise Customer 360 platform on Azure, integrating customer data across multiple business domains to enable unified customer profiles, segmentation, marketing analytics, and personalisation use cases.

• Defined target-state customer data architecture and identity resolution frameworks, designing scalable ingestion pipelines and governed data models to support enterprise marketing measurement and audience activation.

• Managed a cross-functional team of 30 engineers, architects, and analysts, overseeing delivery lifecycle from technical discovery and solution architecture through implementation and production deployment.

• Designed and implemented data ingestion and transformation pipelines using Spark, SQL, and cloud-native services, enabling scalable integration of structured and semi-structured customer and marketing data.

• Established data governance, lineage, and quality frameworks, ensuring regulatory compliance and improving trust in enterprise customer data assets.

• Partnered with marketing, analytics, and technology stakeholders to translate business requirements into scalable customer data solutions, enabling advanced analytics, reporting, and activation across marketing platforms.

• Designed and implemented scalable cloud-based data platforms across AWS and Azure, building ingestion, transformation, and analytics layers to support enterprise reporting, marketing analytics, and data-driven decision-making.

• Led development of a modern analytics platform on Snowflake, implementing ingestion pipelines, governed data models, and reusable transformation frameworks to support enterprise-wide analytics and reporting.

• Developed distributed data processing workflows using Apache Spark and SQL, enabling efficient processing of large structured and semi-structured datasets.

• Integrated global and regional datasets into unified data models, enabling consistent KPI reporting, analytics, and cross-channel marketing performance measurement.

• Applied optimisation techniques including partitioning strategies, query tuning, and workload parallelisation, improving processing performance and reducing data pipeline latency.

• Collaborated with architects, data scientists, and business stakeholders to translate business and analytical requirements into scalable data architecture and pipeline designs.