Define and drive the strategy, roadmap, and architecture for Dyson’s enterprise data platforms.
Design, build, and operate scalable data platform services for analytics, reporting, AI, and machine learning workloads.
Implement cloud-native data platform solutions across GCP and AWS where required.
Establish platform standards, governance frameworks, security controls, and operational best practices.
Build and manage services for data ingestion, transformation, orchestration, storage, and consumption.
Enable self-service data capabilities for data engineers, analysts, and data scientists.
Ensure platform availability, performance, security, compliance, and cost efficiency.
Drive automation through Infrastructure as Code, platform engineering, and operational tooling.
Lead platform modernization and emerging technology adoption across analytics and AI.
Mentor engineers and develop technical capabilities within the team.
Manage platform lifecycle, operational support, incident management, and continuous improvement.
Drive FinOps and platform optimization initiatives.
Requirements
Degree in Computer Science, Software Engineering, Information Technology, Data Engineering, or a related discipline.
Significant experience building and operating enterprise-scale data platforms.
Strong understanding of modern data architecture, data engineering, and analytics ecosystems.
Experience with cloud-native data platforms and services in Google Cloud Platform, AWS, or Azure.
Experience with data warehousing, data lakes, lakehouse architectures, and large-scale data processing.
Strong understanding of data governance, security, privacy, and compliance requirements.
Experience with data orchestration, automation, platform engineering, Infrastructure as Code, CI/CD, and cloud automation frameworks.
Experience supporting analytics, business intelligence, AI, and machine learning workloads.
Strong stakeholder management, communication, technical leadership, and problem-solving skills.
Desirable experience with BigQuery, Databricks, Snowflake, real-time data streaming, event-driven architectures, data catalogs, metadata management, data governance solutions, enterprise AI and machine learning platforms, cloud certifications, and multi-cloud environments.
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