Senior Manager Cloud Data Platforms

We are seeking an experienced Data Platform Engineering Manager to lead our team responsible for designing, developing, and delivering our enterprise data platform and reusable data products.

This role is critical to ensuring scalable, secure, and high-performing data capabilities that power insights, decision-making, automation, and future AI use cases.

The successful candidate will have a proven track record of leading Data Engineering or Data Platform teams in complex, cloud-native environments and delivering scalable data pipelines and reusable data products.

They will also have experience implementing engineering standards, code reviews, and reusable frameworks, as well as a strong technical knowledge of modern data platforms such as Snowflake and AWS.

1. Oversee the end-to-end development lifecycle - from backlog planning through to tested and automated deployment - ensuring quality, reusability, and platform alignment.
   
2. Lead the engineering team building data pipelines, models, and reusable components using Snowflake and AWS-native tools (Glue, S3, Lambda).
   
3. Translate business and analytical requirements into well-designed data products aligned to domain needs and platform strategy.
   
4. Oversee scalable data models, semantic layers, and data marts to support insights and CX solutions.
   
5. Embed strong solution design practices and partner with architects to ensure alignment with enterprise patterns.
   
6. Manage and coach a multidisciplinary team including Data Engineers, Solution Designers, DevOps Engineers, and Data Test Engineers.
   
7. Ensure development standards, governance (naming, logging, lineage), and best practices are consistently applied.
   
8. Drive adoption of test-driven development, CI/CD, and automation across the team.
   
9. Collaborate with Data Ops to ensure smooth deployment, observability, and post-release support.
   
10. Partner with Performance Monitoring & Insights and CXM Ops teams to ensure platform capabilities meet reporting and customer intelligence needs.
    
11. Continuously improve engineering maturity, velocity, and reusability of data products across service lines.
    
12. Contribute to GenAI and Agentic AI readiness by building modern, AI-enabled data foundations.
    
13. Inspire and lead a high-performing engineering team, fostering accountability, technical excellence, and continuous learning.
    

• 7+ years in Data Engineering, including 3+ years in leadership or senior technical roles
  
• Proven experience leading Data Engineering or Data Platform teams in complex, cloud-native environments
  
• Demonstrated success delivering scalable data pipelines and reusable data products
  
• Experience implementing engineering standards, code reviews, and reusable frameworks
  
• Track record enabling insights, CX, or AI teams through platform engineering
  
• Strong technical knowledge of modern data platforms (Snowflake, AWS)
  
• Deep understanding of data pipeline design, ELT/ETL patterns, and data modelling principles
  
• Proficiency in CI/CD, testing frameworks, and deployment automation
  
• Ability to lead multidisciplinary technical teams across development lifecycles
  
• Excellent stakeholder communication and collaboration skills
  
• Experience setting standards and best practices for engineering teams
  
• Experience in Agile delivery environments, virtual squads, or service-aligned team models
  
• Exposure to real-time data ingestion and event-driven architecture
  
• Contribution to GenAI or advanced analytics-enabling platforms
  
• Familiarity with data mesh or federated data product design
  
• Knowledge of data governance, metadata, and lineage practices
  
• Understanding of public sector or regulated industry environments
  

We value diversity and encourage people with different experiences and backgrounds to apply. We commit to our people's development so they can thrive.