Shift Pattern:
Standard 40 Hour Week (United Kingdom)Scheduled Weekly Hours:
40Corporate Grade:
D - Assistant Vice PresidentReporting Line:
(UK Division) Information TechnologyLocation:
UK-LondonWorker Type:
PermanentAbout the London Metal Exchange:
The London Metal Exchange is the world centre for the trading of industrial metals – the majority of all non-ferrous metal futures business is transacted on our platforms. A member of HKEX Group, the LME brings together participants from the physical industry and the financial community to create a robust and regulated market where there is always a buyer and a seller, where there is always a price and where there is always the opportunity to transfer or take on risk – 24 hours a day.
The Sustainability and Physical Markets team (“SPM”) is responsible for the strategy, delivery, and operation of the LME’s physical market and sustainability activity. This incorporates both strategic development of all areas of the LME’s physical ecosystem including future commercialisation, sustainability, overall management of physical market relationships including producers of LME-listed brands, and the operation of the LME’s physical network including LME warehousing, operation of the LME approved brands list, LME responsible sourcing, and LMEpassport.
Purpose of Role:
This role is a critical part of the new technology team supporting the SPM team, providing key data engineering activities including enterprise data model updates, pipeline management, analysis & visualisation engineering, supporting the SPM technology team, external SPM vendors and working with the wider technology teams to integrate SPMs new and existing services effectively. The role will be working closely alongside the SPM business team and technology vendors to engineer solutions supporting their strategic roadmap.
This is a high-impact role for a candidate who is passionate about engineering excellence, working directly with business teams to create solutions and enabling tangible value through technology. This role is a full-stack engineering role (covering frontend, backend, data, infrastructure) and collaborates on solution design, implementation, deployment, testing and support.
Responsibilities:
Design, implement, and maintain robust data pipelines and infrastructure to support sustainability data modelling, analysis, and critical data workloads, ensuring reliability and scalability.
Ensure the robustness and quality of data workloads using Python and modern data engineering practices, including automated validation, monitoring, and comprehensive testing.
Own and evolve the team’s data models and integrations, supporting seamless data flows across platforms such as Salesforce, LMEpassport, and LMEsword, in collaboration with internal stakeholders and external vendors.
Provide internal data analysis and reporting to support business and technology objectives.
Lead incident analysis and root cause investigations for data-related issues, implementing improvements to enhance system stability and performance.
Represent SPM data needs to the internal enterprise data team, managing SPM-related components of the Enterprise Data Model and contributing to data governance.
Develop and maintain the future data roadmap for SPM, considering advancements in AI, external data products, and evolving data technologies.
Act as a liaison between technical teams and non-technical stakeholders, ensuring clear and effective communication of project status, risks, and requirements.
Ensure all technical documentation is accurate, up-to-date, and accessible to relevant stakeholders.
Academic and Professional Qualifications Required:
Required: Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a closely related field.
Advantageous: Certifications or substantial hands-on experience with modern data pipeline tools (e.g., Apache Airflow, Spark, Kafka, dbt, or similar).
Desirable: Familiarity with financial services regulatory frameworks (e.g., MiFID II, GDPR, SOX) and best practices for data governance
Required Knowledge and Level of Experience:
Experience: Minimum 5 years in data or software engineering, with demonstrable lead involvement in at least one production-grade data system within financial services or a similarly regulated industry.
Data Quality: Proven ability to validate and govern data pipelines, ensuring data integrity, correctness, and compliance.
Full-Stack Engineering: Hands-on experience with Java (Spring Boot), React, and Python, covering backend, frontend, and data engineering.
Data Engineering Tools: Proficient with modern data engineering and analytics platforms (e.g., Apache Airflow, Spark, Kafka, dbt, Snowflake, or similar).
DevOps & Cloud: Experience with containerisation (Docker, Kubernetes), CI/CD pipelines, and cloud platforms (e.g., AWS, Azure, GCP) is highly desirable and increasingly standard in the industry.
Skills set and Core Competencies Required for Role:
Programming & Data: Strong proficiency in Python, SQL, and Postgres (or equivalent RDBMS) for data engineering and analytics.
DevOps: Skilled in building, deploying, and maintaining containerised applications using Docker and Kubernetes; experience with infrastructure as code (e.g., Terraform, Helm) is a plus.
Linux: Solid understanding of Linux systems administration and scripting.
Testing: Commitment to automated testing (unit, integration, end-to-end) and quality assurance throughout the software delivery lifecycle.
Monitoring & Observability: Familiarity with monitoring, logging, and alerting tools (e.g., Prometheus, Grafana, ELK stack) is advantageous.
Analytical & Problem-Solving: Excellent analytical and problem-solving skills, with a focus on delivering measurable business impact.
Agile & Collaboration: Ability to work effectively in agile, cross-functional teams, and contribute to collaborative solution design.
Communication: Outstanding communication skills, able to engage and translate between technical and non-technical stakeholders.
Personal Qualities:
Curiosity & Proactivity: Demonstrates a passion for continuous learning, improvement, and staying current with industry trends.
Collaboration: Works effectively across departments and disciplines, building strong relationships with both technology and business colleagues.
Outcome-Driven: Motivated by delivering real-world outcomes, improving enterprise value, and supporting business strategy.
The LME is committed to creating a diverse environment and is proud to be an equal opportunity employer. In recruiting for our teams, we welcome the unique contributions that you can bring in terms of education, ethnicity, race, sex, gender identity, expression and reassignment, nation of origin, age, languages spoken, colour, religion, disability, sexual orientation and beliefs. In doing so, we want every LME employee to feel our commitment to showing respect for all and encouraging open collaboration and communication.
