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About This Role
Texas, United States of America
Job Family Group:
Information Technology (IT)
Worker Type:
Regular
Posting Start Date:
June 10, 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 10 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 directly with analysts, traders, and other business stakeholders
Additional Preferred Qualifications
- Exposure to commodity or financial trading environments
- Understanding of market fundamentals, supply‑demand dynamics, or risk concepts
- Experience with MLflow, feature stores, or vector databases
- Familiarity with working in regulated or risk‑sensitive environments
What we offer
The base salary range for this position is $149,000 \- $223,000 per year . Individual pay will be based on various factors, such as relevant work experience, education, certifications, skill level, seniority, and internal equity.
For regular full\-time or regular part\-time employees of the Company (participating companies as listed in the Summary Plan Description), insurance coverage options include medical, dental, vision coverage, life Insurance, Business Travel Accident Insurance, and Occupational Accidental Death Benefit programs. Employees also participate in a company pension plan and a 401(k) plan. Paid leave includes up to 6 weeks of paid vacation time, up to 11 paid holidays, and parental leave offering 16 weeks of paid leave for birthing parents, and 8 weeks of paid leave for non\-birthing parents.
Additionally, employees are eligible for short\-term disability leave for up to 26 weeks at 100% or 50% of base pay as well as Long\-Term Disability insurance. Shell also offers other compensation such financial reimbursement for adoption, wellness, education, and personal learning expenses, and some roles are eligible for discretionary long\-term incentives. For interns, eligible benefits include medical, dental, and vision coverage, life insurance, Business Travel Accident Insurance, and Occupational Accidental Death Benefit programs; participation in a 401(k) plan; and paid leave for up to 11 paid holidays. Additional information on Shell’s US benefit programs can be found at https://www.shell.us/careers/about\-careers\-at\-shell/rewards\-and\-benefits.html .
You bring your skills and experience to Shell and in return you work with talented, committed people on one of the most important challenges facing our planet. You’ll have the opportunity to develop the skills you need to grow in an environment where we value honesty, integrity, and respect for one another. You’ll be able to balance your priorities as you become the best version of yourself.
- Progress as a person as we work on the energy transition together
- Continuously grow the transferable skills you need to get ahead
- Work at the forefront of technology, trends, and practices
- Collaborate with experienced colleagues with unique expertise
- Achieve your balance in a values\-led culture that encourages you to be the best version of yourself
- Benefit from flexible working hours, and the possibility of remote/mobile working
- Perform at your best with a competitive starting salary and annual performance related salary increase – our pay and benefits packages are considered to be among the best in the world.
- Take advantage of paid parental leave, including for non\-birthing parents
- Join an organization working to become one of the most diverse and inclusive in the world. We strongly encourage applicants of all genders, ages, ethnicities, cultures, abilities, sexual orientation, and life experiences to apply
- Grow as you progress through diverse career opportunities in national and international teams
- Gain access to a wide range of training and development programs
We'd like you to know that Shell has a bold goal: to become one of the world’s most diverse and inclusive companies. You can get to know more about how we're working towards that goal, click here .
Shell in The United States
Shell has been in the US for more than 100 years, leading the sector in energy, petrochemicals and refined products. Today, we provide millions of Americans with the energy needed to heat and cool their homes and power the economy.
We operate in all 50 states, from our Deepwater platforms in the Gulf of America to the Pennsylvania Chemicals complex and our miles of pipelines throughout the US.
We reach our customers through our 13,000 branded retail stations, and we are number 1 in gasoline sales. We also own the \#1 brand of motor oil in the U.S. – Pennzoil® – made from natural gas.
We are excited to play a key role in the move to net carbon emissions while providing the oil and gas needed by society for many decades to come.
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DISCLAIMER:
Please note: We occasionally amend or withdraw Shell jobs and reserve the right to do so at any time, including prior to the advertised closing date. Before applying, you are advised to read our data protection policy. This policy describes the processing that may be associated with your personal data and informs you that your personal data may be transferred to Shell/Shell Group companies around the world. The Shell Group and its approved recruitment consultants will never ask you for a fee to process or consider your application for a career with Shell. Anyone who demands such a fee is not an authorised Shell representative and you are strongly advised to refuse any such demand. Shell participates in E\-Verify. All qualified applicants will receive consideration for employment without regard to race, color, sex, national origin, age, religion, disability, sexual orientation, gender identity, protected veteran status, citizenship, genetic information or other protected status under federal, state or local laws. Shell is an Equal Opportunity Employer \- Minorities/Females/Veterans/Disability. As a US Federal Contractor, hiring selections are subject to periodic audit review and documentation of your selections should be maintained for a period of three calendar years. It is the policy of Shell in the U.S. (“Shell”) to provide equal opportunity to all individuals, employees and all qualified applicants for employment consistent with employment requirements and qualifications. Shell prohibits discrimination based on race, color, sex, national origin, age, religion, disability, sexual orientation, gender identity, veteran status, citizenship, genetic information, or other protected status under federal, state or local laws. All employees are expected to support this policy and contribute to an environment of equal opportunity. If you need an accommodation for a disability during the resourcing process, please speak with an HR representative.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Shell Energy Retail, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 266 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Shell Energy Retail AI Hiring
Shell Energy Retail has 1 open AI role right now. They're hiring across Data Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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