Job Description
Own the strategy and execution of best-in-class data engineering to deliver state-of-the-art data products and services at scale. Build and operate a modern estate on Azure + Snowflake , centred on event-driven architectures and high-throughput ingestion/pipelines that feed analytics, risk, and AI/ML safely and cost-effectively. Establish the standards, tooling and talent model that convert complex trading data into fast, reliable, governed, and reusable products, aligned to the firm's semantic/knowledge-graph backbone. Define and execute the global data engineering strategy (ingest govern serve observe), aligned with enterprise architecture and governance. Standardise event patterns (Kafka/Flink), ELT (dbt/Spark/SQL), and serving layers (APIs/SQL/Graph) across regions. Build and coach high-performing squads ; manage the engineering capacity plan ; anticipate peaks and scale out via vetted staff-augmentation partners without lowering the bar. Run an objective skills framework, hiring rubric, and career paths; ensure global follow-the-sun support on critical flows. Partner with Trading, Risk, Ops/Logistics, Finance/Settlement and Compliance to prioritise a value-backlog; communicate trade-offs on latency, cost and control. Align with Architecture on ontology/knowledge-graph mapping; with Governance on evidence and controls; with Platform/Operations on environments, access and DR. Reliability: SLOs met on market-critical paths; deterministic replay proven quarterly; MTTR trending down. Speed & Reuse: Time-to-first-value for new products reduced by >50% ; adoption of golden paths/templates across squads >60% . Cost: Unit economics (cost per product/feature/inference) visible; 15-25% cost-to-serve reduction through optimisation/deprecation. Compliance: Zero critical audit findings on lineage, access, retention; automated evidence packs. Talent & Capacity: Bench strength in core skills; surge capacity activated without quality or security regressions. 12+ years in data engineering/platform roles, 5+ years leading multi-region teams in real-time, regulated environments (ideally commodity trading/energy/financial markets). Global Head of Data: strategy, budget, risk appetite, and executive reporting. Lead Data Solution Architect & team: domain roadmaps, solution assurance, reuse/adoption metrics. Platform Ops and Data Engineering: CI/CD, observability, identity/secrets, DR/BCP and FinOps. Data and AI Governance, Risk, Compliance & Internal Audit: model risk, evidence automation, regulatory readiness, fine-grained FinOps Enablement. Business Lines: Trading, Risk, Ops/Logistics, Asset Ops/SCADA, Finance/Settlement, Market Analysis, value mapping and SLO reviews. Commodity depth: Power/gas, Oil, Derivatives, time-series operational dashboards. Knowledge-graph awareness: semantic layers (entity/relationship drill-through, lineage/impact views, consistent business terms). Advanced geospatial: Mapbox/Leaflet, tiling strategies, clustering, and projection choices for assets, routes, and weather overlays. LLM-assisted UX: Patterns for in-workflow assistants, retrieval-augmented explanations, and safe inline summarisation of alerts/incidents. Design for low-latency streams: Live updates, batching, and diff-only rendering for high-frequency market data. BI engineering partnership: Custom visual specs, semantic model constraints (star/snowflake), and row-level security/RBAC considerations. Security basics: Understanding of ABAC/RBAC, PII handling, export controls, and auditability of user actions. Performance tuning: p95/p99 render targets, bundle hygiene, virtualisation of large grids, and caching strategies. Quant empathy: Comfortable discussing VaR/PFE math at a conceptual level to avoid misrepresenting risk semantics. Prototyping breadth: Interactive prototypes wired to mock APIs; ability to script lightweight data fixtures. Change management: Training kits, walkthroughs, and adoption campaigns for front-office and operations users. Highly numerate, rigorous, and resilient in problem-solving. Ability to prioritize, multitask, and deliver under time constraints. Strong written and verbal communication in English. Self-motivated, proactive, and detail-oriented. Comfortable working under pressure in a fast-paced environment. Excellent communication skills, ability to explain technical topics clearly. Team player with ability to collaborate across engineering, quant, and trading teams.