Sr. AI & Data Engineer - Trading Analytics

About the role

Texas, United States of America Job Family Group: Information Technology (IT) Worker Type: Regular Posting Start Date: June 11, 2026 Business Unit: Projects and Technology Experience Level: Experienced Professionals Job Description: What's the role Shell is seeking a Sr. AI & Data Engineer - Trading Analytics (also known internally as Data Engineering Lead) to join its team in Houston, TX. This role is ideal for an AI Engineer with a strong foundation in data engineering who can design and deliver AI-driven front-office analytics and GenAI/agentic solutions, collaborating closely with traders and trading analysts. Shell Trading & Supply is one of the world's leading energy and commodity trading organizations, operating across crude, refined products, gas, LNG, power, and environmental products. Trading sits at the heart of Shell's energy transition, requiring rapid, data‑driven decision making under complex market conditions. Within Trading & Supply, the Crude & Products business plays a critical commercial role, operating in highly regulated, fast‑moving markets where high‑quality analytics, data engineering, and AI are key competitive differentiators. Our focus is to deliver secure, reliable, and trader‑centric digital solutions that simplify workflows, enhance insight, and drive commercial value without compromising risk, safety, or controls. As a Sr. AI & Data Engineer in Trading Analytics, you will work directly with front‑office teams to design, build, and productionize AI‑driven analytics over market pricing and fundamental data. You will combine Databricks‑based data engineering, statistical and econometric techniques, and modern GenAI/agentic workflows to deliver insight at speed. This is a hands‑on individual contributor role with a strong bias toward practical delivery. You will operate in a high‑touch, hybrid model, iterating rapidly with users on‑desk, while engineering solutions to production‑grade standards using modern CI/CD and governance practices. Also, this role is rapid prototyping, iterative learning, and incremental production hardening, balanced with adherence to engineering, security, and governance frameworks to maintain speed and reliability. Accountabilities: Front‑Office Analytics & Insight Design, build, and deliver AI‑driven analytics for traders and analysts, including seasonality analysis, correlation studies, regression models, forecasting, and scenario modelling over market pricing and fundamentals data Work closely with traders and analysts to translate ambiguous business questions into clear analytical problem statements and working solutions Clearly communicate analytical outputs and AI‑generated insights to commercial stakeholders in a concise and actionable manner Data Engineering & Platforms Build and maintain scalable, reusable data pipelines on Databricks using PySpark/Spark, SQL, Delta Lake, and Unity Catalog Support ingestion, modelling, and transformation of large‑scale time‑series pricing and fundamentals datasets Optimize pipelines for performance, reliability, and cost efficiency, following platform and data governance standards AI, GenAI & Agentic Solutions Build and enhance GenAI and agent‑based solutions to support trading analytics, including: Retrieval‑Augmented Generation (RAG) Prompt engineering Agent orchestration using frameworks such as LangGraph Tool calling and guardrails Integrate LLM‑based workflows with structured trading and market data to augment analysis, insight generation, and decision support Prototype solutions quickly, gather user feedback, and help harden selected use cases for production deployment Production, Quality & Operations Contribute to production‑ready analytics and AI solutions with testing, documentation, versioning, and basic observability Follow established CI/CD and DevOps practices, including use of Git‑based workflows and automated testing Support governance requirements such as PII handling, data lineage, and auditability in line with Trading & Supply standards What you bring Must have legal authorization to work in the US on a full-time basis for anyone other than current employer Bachelor's degree or equivalent relevant years of experience At least 7 years of relevant experience Hands‑on experience with Databricks and/or Spark (PySpark, SQL, Delta Lake; Unity Catalog desirable) Proven data engineering skills, including pipeline development, data modelling, and performance optimization Solid foundation in statistics, econometrics, or data science, with experience applying these techniques to time‑series or market‑style datasets Practical experience building or contributing to LLM‑based solutions, including prompt engineering and retrieval‑based approaches Familiarity with GenAI frameworks and tooling (e.g., LangGraph or similar orchestration patterns) Experience working in collaborative engineering teams using Git and CI/CD pipelines Strong communication skills and the ability to work dir