• Reduce latency in AI-powered financial answers • Simplify fragmented and legacy-heavy architecture • Improve cross-dataset querying and screening • Ship new datasets and product capabilities without increasing systemic complexity • Optimize orchestration between LLM workflows, structured datasets, and services • Introduce performance benchmarks, observability standards, and SLO discipline • Improve reliability and correctness of AI-backed financial responses • Reduce fragmentation across datasets and services • Partner with data platform teams to define scalable dataset integration patterns • Remove legacy complexity while continuing to ship • Contribute directly to backend and frontend systems powering financial data experiences • Define durable contracts between AI-generated outputs and frontend consumers • Ensure performance and usability at both API and product layers • Drive technical initiatives spanning multiple engineering teams • Align stakeholders on service boundaries and system design decisions • Raise the bar for system design, reliability, and operational rigor.