Partner with Enterprise Architects to define and evolve the trade surveillance technical roadmap, ensuring alignment with firm-wide architecture standards, data strategy, and regulatory expectations.
Own end-to-end architecture for surveillance scenario development, alert generation, case management integration, and downstream investigator tooling.
Define reference architectures, design patterns, and engineering guardrails for the surveillance engineering organization.
Scenario & Alert Engineering
Lead the design and implementation of trade surveillance scenarios and alerts across asset classes (equities, fixed income, FX, derivatives) covering market abuse typologies such as spoofing, layering, wash trades, front-running, insider trading, and cross-product manipulation.
Drive development of scenarios and analytics in both Q/KDB (current state) and Python/PySpark (target state).
Establish reusable frameworks for scenario authoring, parameter tuning, backtesting, threshold calibration, and false-positive reduction.
Platform Modernization & Cloud Migration
Lead migration of the trade surveillance platform to the new cloud infrastructure (AWS / Azure / GCP), including compute, storage, streaming, and orchestration layers.
Architect and oversee the migration of scenarios, libraries, and frameworks from Q/KDB to Python/PySpark on distributed compute (Spark, Databricks, EMR, or equivalent), ensuring functional parity, performance, and auditability.
Design for scale — alert generation across billions of order and trade events per day — with focus on throughput, latency, cost optimization, and resilience.
Data & Engineering Excellence
Define data models and ingestion patterns for orders, executions, market data, reference data, communications, and news/social feeds.
Champion engineering best practices: CI/CD, IaC, automated testing of surveillance logic, observability, lineage, and reproducibility of alerts (critical for regulatory defensibility).
Collaborate with Data Engineering, DevOps, and InfoSec on secure-by-design implementations.
Identify and prototype applications of Generative AI and ML in surveillance — narrative generation for alerts, investigator copilots, anomaly detection, communications surveillance (NLP), and intelligent triage.
Evaluate vendor and open-source capabilities and guide build-vs-buy decisions.
Leadership & Stakeholder Management
Mentor senior engineers and tech leads; conduct design reviews and uphold architectural quality.
Engage with Compliance, Front Office Supervision, Internal Audit, and Regulators on technical aspects of the surveillance program.
