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:
Using innovative, collaborative, and client focused approaches to data extraction, aggregation, and standardization, the Sustainable Finance Data team is responsible for enhancing ESG content for the Bloomberg Terminal and Enterprise products. This includes company-reported and normalized metrics, industry-specific metrics, proprietary ESG scores, climate and regulatory data. Our Sustainable Finance Data is displayed alongside fundamental data, backed by news for context, and used to power a growing suite of sustainable finance products.
What’s the role?
We’re looking for a hands-on data manufacturing manager to lead and optimize processing for our Sustainable data operations. You’ll be responsible for workflows across our global vendor ecosystem, from task design and routing to quality control and automation, ensuring we deliver high-quality AI-ready data at scale. In this role you will partner closely with Product, Engineering, and internal Operations to re-design workflows, embed automation, and reduce manual effort. You will also build and manage a high-performing vendor portfolio with clear SLAs, robust business intelligence/observability, client engagements to showcase data quality and a strong culture of continuous improvement.
We’ll trust you to:
- Design and supervise the full data manufacturing process for vendor workflows by mapping and refining end-to-end processes for ESG Data including but not limited to acquisition, extraction, enrichment, QC and issue resolution
- Translate business goals into concrete process designs, standard work, and clear operating models across vendors and locations to drive improvements around coverage, timeliness and quality
- Optimize flow and capacity through modelling and workload balancing, identifying systemic bottlenecks or constraints, and prioritizing fixes that improve the overall system
- Standardize and automate workflows in partnership with Engineering, Product, and data management teams to reduce manual effort and increase service stability and delivery
- Develop and maintain observable production processes to facilitate quality management through thorough metric design, statistical analysis, and statistical monitoring
- Own clear reporting of Data Production and Systems metrics for data operations in partnership with data management and engineering teams.
- Enable AI-readiness by ensuring vendor-produced data is structured and compatible with downstream automation use cases
- Lead operations at scale, including capacity planning, prioritization across parallel projects, and fast resolution of incidents and emerging risks
- Build strong vendor partnerships with clear communications, structured business reviews, targeted remediation plans, and incentives tied to measurable outcomes
You'll need to have:
- 5+ years of experience in process engineering data operations, or a similar field, including people leadership and/or vendor-facing management
- Proven experience running sophisticated operations at scale, balancing demand and capacity, handling queues, and meeting SLAs.
- Strong analytical and technical skill, confidence with data pipelines and operational metrics, and comfort with using SQL or similar tools to investigate issues and quantify impact
- Proven track record designing and improving workflows, such as simplifying steps, analyzing processes, or introducing exception-based monitoring
- Experience leading teams across varying regions, languages, and time zones, with clear performance frameworks
- A track record of partnering with Engineering and Product to improve process adoption success across tooling, workflow or automation changes
- Excellent communication skills to influence without authority across a matrixed, global organization
- Willingness and ability to travel for vendor governance, audits, and other business needs when required
- Please note: years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.
We’d love to see:
- Background in Industrial Engineering, Operations Research, Process Engineering, or equivalent practical experience
- Experience with process improvement methodologies and implementation to reduce effort, rework, or cycle time
- Hands-on experience with statistical methods or experimental design to improve benchmarks and validate process changes
- Exposure to automation technologies and how to deploy them in production environments
- Familiarity with modern data stacks and observability (ETL/ELT, data quality monitoring, logging/metrics platforms) in a production setting
- Experience working with financial data, sustainable investing, or adjacent capital markets content.
- Experience working in environments that combine human-in-the-loop operations with sophisticated analytics/ML
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!
