• Design, build, and maintain scalable data pipelines primarily within Google Cloud Platform • Develop integration and transformation workflows between cloud data services and on-prem Oracle databases • Work closely with trading, risk, and analytics teams to understand data requirements and deliver real-time and batch data solutions • Optimise and monitor performance of data systems to support latency-sensitive trading applications • Collaborate with cross-functional teams using Agile/Scrum methodologies to deliver business-critical data projects, provide technical assistance to the team • Ensure robust data governance, lineage, and compliance (including MiFID II, FCA, and other regulatory standards) • Automate data workflows using Terraform, CI/CD pipelines, and containerisation tools (Docker/Kubernetes)

• Build core components of the data platform • Develop reusable data access layers • Implement schema enforcement and validation gates • Build the observability framework to monitor pipeline health • Participate in design discussions and code reviews

• Lead the design and implementation of internal SDKs and self-service frameworks • Shift from "pipeline building" to "platform engineering," creating reusable patterns for batch and real-time event processing • Take full ownership of the cost-effectiveness of the Databricks ecosystem • Ensure the platform remains performant as volume grows • Implement Schema-on-Write validation and Data Contracts to ensure data meets strict quality standards • Partner with the Data Architect & Data Stewards to enforce data privacy and security standards • Champion the use of AI-assisted development tools to accelerate the engineering lifecycle • Mentor engineers on distributed computing best practices

• Define how data is understood, accessed, and used by AI systems across the company. • Build clean, reusable analytical data layers in BigQuery. • Move business logic from BI into the warehouse. • Define metrics, dimensions, and semantic consistency. • Enable reliable AI ↔ data interaction. • Design schemas + instructions so AI produces correct outputs. • Test and refine real AI usage (not theory). • Implement data-layer access control (not BI-layer). • Ensure consistent behavior across BI, AI, and internal tools. • Ensure metric reconciliation across different data sources. • Prevent sensitive data leakage and uncontrolled query cost. • Replace manual data workflows with AI-driven processes. • Build agents for reporting, validation, and internal analytics.