• Build and manage data pipelines: ingestion from APIs/files, transformations across layers, schema evolution, and collectors from 10+ external sources • Design semantic models and hybrid search for AI; own star schema with bridge tables and performance optimization • Enforce governance: access controls, licensing registry, quality monitoring, and retention for GDPR/CCPA • Manage cloud infrastructure, monitoring, SLAs, cost optimization, and CI/CD • Deliver client views, multi-cloud exports, and partner with delivery teams for dashboards and AI tools

• Define, implement, and govern enterprise data architecture standards, policies, and frameworks across the assigned business domain • Ensure data architecture artefacts, data products, and data deliveries comply with enterprise governance, quality, and regulatory requirements • Lead domain data modelling activities and ensure alignment with enterprise and global data models • Develop and maintain current, transition, and target-state data architectures and data flows across business domains • Drive architecture decisions for enterprise data platforms, analytical solutions, and strategic data initiatives • Establish and facilitate data governance forums, architecture review boards, and decision-making processes • Collaborate with business stakeholders, data owners, technology teams, and enterprise architects to align on data standards and architecture roadmaps • Provide architectural leadership for regulatory reporting and business transformation initiatives across Risk, Finance, Treasury, Compliance, or Transaction Banking domains • Review and approve solution and data architecture artefacts to ensure adherence to enterprise standards and best practices • Monitor emerging technologies and modern data architecture trends, recommending innovative solutions that support the organization's strategic objectives • Contribute to the development of enterprise data strategy, governance capability, and future-state architecture roadmaps • Promote the adoption of enterprise data governance, metadata management, and data quality practices across the organization

• Own and self-heal our fleet of web scrapers • Keep daily scraping runs stable—monitoring, alerting, retries, and graceful handling of upstream failures • Use LLMs for resilient parsing and entity extraction from messy or changing HTML • Own and optimize the serving layer and the ETL/ELT pipelines feeding our BigQuery warehouse • Build our reporting infrastructure—data models, transformations, and dashboards • Drive data quality through rule-based checks and ML/LLM-based anomaly detection • Build and maintain production-grade MCP servers and agentic workflows

• Develop, evolve, and maintain scalable data pipelines in a Google Cloud Platform (GCP) environment • Design and implement data ingestion, processing, and storage solutions using services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Functions • Develop and optimize ETL/ELT processes, ensuring performance, reliability, and data quality • Create data models to support analytics, Business Intelligence, and Data Science initiatives • Monitor, diagnose, and optimize the performance of pipelines and data processing workloads • Implement best practices for governance, security, observability, and data quality • Collaborate with Software Engineering, Analytics, Data Science, and business teams to understand requirements and develop data-driven solutions • Contribute to the evolution of the data architecture, proposing continuous improvements in scalability, cost, and performance • Follow development best practices, automation, and continuous integration/continuous deployment (CI/CD).