Senior Data Management Professional – Data Engineer – Commodities Data
Location: London
Business Area: Data
Description & Requirements
Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology – quickly and accurately. We apply problem-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
Our Team
The Commodities Data team is looking for a highly experienced Senior Data Management Professional to help lead the next generation of our data platform. This role requires a strong data engineering foundation combined with deep ownership of data quality, where quality is built directly into pipelines, systems and architecture rather than managed as a separate function. This role is designed for a top‑tier individual contributor who thrives in complex environments and consistently delivers high‑impact, scalable solutions.
The Role
You will be responsible for designing and evolving data systems that power Tier 1 datasets, improving reliability, reducing technical debt and modernizing legacy workflows. This includes building advanced ETL pipelines, implementing intelligent automation and developing robust data quality controls and monitoring frameworks to ensure data accuracy, completeness and timeliness.
In addition, you will play a key role in defining and delivering the data quality vision for our datasets. This includes evolving fit‑for‑purpose quality metrics, understanding how clients consume data across Bloomberg products and aligning data with both client needs and Bloomberg’s commercial strategy. You will also influence data governance practices and lifecycle management across teams to ensure long‑term data integrity and scalability.
You will collaborate closely with Product, Engineering and domain experts to define and execute on strategic data initiatives. In addition to hands‑on development, you will act as a technical leader within the team by owning end‑to‑end solutions, influencing architecture decisions and mentoring others.
We are looking for someone who operates at a high bar of technical excellence, takes ownership of both data systems and data quality outcomes, and uses modern technologies including AI and machine learning to enhance data workflows and extract additional value from our datasets.
We’ll Trust You to:
- Build and maintain highly scalable, resilient and observable data pipelines supporting critical Commodities datasets
- Modernize legacy workflows, reduce technical debt, and improve performance, reliability, and maintainability.
- Design automated pipeline controls for validation, monitoring, schema change, exception handling, and data integrity.
- Develop workflow orchestration, alerting, observability, and remediation processes.
- Translate business and client needs into engineering‑ready requirements and scalable technical solutions.
- Partner with Engineering on platform evolution, architecture, tooling, system design, and reliability.
- Apply automation, AI, machine learning, or statistical techniques to improve ingestion, enrichment, validation, and monitoring.
- Own data migrations, workflow redesigns, and technical transformation initiatives.
- Establish standard methodologies for pipeline design, code quality, testing, documentation, version control, and operational handover.
- Influence data modelling, metadata, lineage, and lifecycle management practices from a technical implementation perspective.
- Mentor team members and set the standard for technical execution, design thinking, and engineering rigor
You’ll need to have:
- A bachelor’s degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree‑equivalent qualifications
- 4+ years of experience designing and building scalable data solutions, ETL pipelines, data workflows, and monitoring frameworks.
- Strong hands‑on experience with Python or similar programming/scripting languages.
- Experience with querying structured, semi‑structured, and unstructured datasets.
- Experience with workflow orchestration, observability, monitoring, alerting, and scalable architecture design.
- Ability to analyze, refactor, and modernize legacy systems.
- Strong understanding of data lifecycle management, data integration, data modelling, data profiling, and data governance.
- Experience building automated controls and reliability frameworks into data pipelines.
- Strong communication skills with the ability to collaborate across Data, Engineering, Product, Vendors, and other stakeholders.
We’d love to see:
- Bloomberg Terminal, BQL, Enterprise, or Bloomberg data workflow experience.
- Experience productionizing AI, machine learning, anomaly detection, NLP, classification, or LLM‑assisted workflows.
- Experience with cloud platforms, CI/CD, automated testing, version control, metadata management, lineage, or modern DataOps practices.
- Project management experience with Agile delivery, backlog management, JIRA, or similar tools.
- CDMP certification, or progress toward it, is a plus.
Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law.
Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email amer_recruit@bloomberg.net
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