The successful candidate will manage a team responsible for improving commodities data pipelines, integration patterns, workflow architecture, controls, and operating models. They will reduce operational risk, rationalize legacy workflows, improve automation, and ensure commodities datasets can be onboarded, transformed, validated, monitored, governed, and delivered consistently across the ecosystem.
This role requires strong data management expertise, practical engineering and integration awareness, and the ability to partner effectively with Data Modelling, Data Quality, Product, Engineering, Content Acquisition, Enablement, and regional teams.
We’ll trust you to
Lead modernization of commodities data pipelines across reference data, fundamentals, curves, pricing, index data, and environmental datasets.
Establish scalable patterns for ingestion, transformation, enrichment, validation, publication, monitoring, and exception management.
Assess existing workflows to identify duplication, fragility, inconsistent logic, manual intervention, and opportunities for automation or consolidation.
Re-engineer legacy workflows into scalable, resilient, supportable, and well-controlled operating models.
Define standards for data manufacturing workflows, including documentation,
Partner with Data Quality to embed validation, completeness, timeliness, reconciliation, and exception controls into core workflows.
Work with Data Modelling to ensure pipelines support agreed entities, identifiers, relationships, taxonomies, metadata, and lifecycle rules.
Ensure datasets are delivered with clear ownership, controls, lineage, documentation, support models, and auditability.
Improve monitoring, alerting, root-cause analysis, recovery processes, and preventative controls to reduce operational risk.
Reduce duplicate workflows, redundant processes, manual workarounds, and fragmented ownership across commodities data manufacturing.
Automate and standardize data handling, enrichment, validation, exception management, monitoring, and recovery processes.
Support vendor- and platform-driven change, including schema changes, API migrations, delivery format changes, taxonomy updates, and workflow migrations.
Identify practical opportunities to use AI-assisted tooling and automation to reduce manual mapping, validation, documentation, exception handling, and operational triage while ensuring solutions remain governed, explainable, and supportable.
Manage a team of Data Management Professionals focused on data integration, workflow engineering, automation, operational stability, and scalable commodities data manufacturing.
Set clear priorities and technical direction, balancing modernization, production stability, partner needs, and business-as-usual delivery.
Build team capability in data pipelines, integration patterns, Python, SQL, orchestration, automation, observability, controls, and production support.
Partner with Product, Engineering, Data Modelling, Data Quality, Content Acquisition, Enablement, and regional teams to deliver business-aligned outcomes.
Contribute to global Commodities strategy, workflow standards, integration principles, and operating-model evolution.
You’ll need to have
3+ years of formal people leadership experience, or strong informal leadership
Bachelor's degree or equivalent, preferably in Economics or Finance, or related business / STEM field
Experience leading or materially improving a data manufacturing, data operations, data pipeline, workflow engineering, or integration environment in a commodities, market data, financial data, or similarly complex domain.
Solid understanding of commodities data, including reference data, fundamentals, curves, spot prices, index data, pricing data, environmental commodities, or related market datasets.
Experience designing, improving, or supporting complex data pipelines across ingestion, transformation, enrichment, validation, publication, monitoring, and exception management.
Demonstrable ability to modernize legacy workflows and move teams toward scalable, automated, supportable, and well-controlled operating models.
Strong working knowledge of Python, SQL, orchestration tools, workflow platforms, automation frameworks, observability, and production support practices.
Experience embedding data quality controls, reconciliation, completeness checks, timeliness checks, and exception workflows into production processes.
Ability to work closely with data modelling teams on entity structures, identifiers, taxonomy rules, metadata, and workflow logic.
Experience reducing operational risk through stronger controls, monitoring, documentation, root-cause prevention, and support models.
Demonstrable ability to lead, develop, and coach a team while setting clear priorities and handling senior stakeholder expectations.
Good communication skills, with the ability to translate technical, workflow, and operational risk topics into clear business value.
Experience evaluating or applying AI, automation, or workflow augmentation in a governed and supportable way would be advantageous.
We’d love to see
Experience or knowledge in the Bloomberg terminal, and/or Bloomberg Data workflows
Experience or strong curiosity about data modeling in addition to strong Excel and PowerPoint skills, SQL experience, and Coding experience
Strong people leadership skills, including coaching, prioritization, stakeholder management, and building capability in technical data teams.
Experience working closely with data modelling, data quality, engineering, product, acquisition, and regional partners to deliver scalable data solutions.
If this sounds like you
Apply! If you think we're a good match. We'll get in touch to let you know the next steps!
If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.
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