• Design and maintain a unified data architecture: database schemas, data models, and micro-architecture solutions to ensure scalability and reliability. • Optimize database performance at all levels: indexing, partitioning, clustering, and tuning configuration parameters. • Ensure full compliance with GDPR, UK Data Protection Act, and other relevant regulations: data masking, consent management, retention policies, and privacy impact assessments • Optimize queries, schemas, and indexes where needed • Set up basic data quality checks • Support GDPR and UK data protection requirements, including: Data masking, Access control, Retention policies • Take data notebooks and calculation logic • Turn them into reliable, production-ready pipelines • Ensure scalability, reliability, and reproducibility

• Implement real-time data pipelines with MQTT and Redpanda for stream processing. • Implement offline data pipelines using Dagster for batch processing. • Parse and process binary message formats from various data sources. • Build data warehouses using Postgres, Apache Iceberg, Parquet, and S3. • Design data models that allow for high-performance queries. • Validate and normalize data sources. • Improve local development and CI/CD using modern tooling and GitHub Actions.

• Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale • Design and build production LLM pipelines and infrastructure that power intelligent operations. • Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance • Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation. • Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features. • Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

• Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale • Design and build production LLM pipelines and infrastructure that power intelligent operations. • Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance • Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation. • Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features. • Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices