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:

Data AI contributes to the building of Bloomberg’s AI-enhanced products at scale by curating model training data and enhancing how our internal processes use AI. We provide evaluation and annotation frameworks connecting natural language processing and human judgment in order to elevate the quality, intelligence, and usability of the data that drives our products.

By investing in AI at a strategic level, we expand our practice of engaging with AI to one that is embedded across Data. Our internal processes to take advantage of new AI technologies and strengthen Data’s role in providing robust domain expertise and influential data artifacts to Bloomberg’s products. As a result our clients will continue to have high quality data and access to new types of datasets.

The Role:

As a Data Engineer within Data AI, you will build and evolve the infrastructure, data pipelines, and operational tooling that power scalable AI and data workflows. You will enable reliable data collection, annotation, training, and evaluation processes by developing systems that improve data quality, operational visibility, and workflow efficiency. Through automation, observability, and platform engineering, you will help create the foundations that allow teams to deliver data and AI products with confidence and at scale.

We’ll trust you to:

• Design, build, and maintain scalable data pipelines that support data collection, annotation, training, evaluation, analytics, and reporting workflows.
• Develop and operate systems for dataset management, storage, versioning, and lifecycle governance to ensure reliable and reproducible AI workflows.
• Implement monitoring, observability, and alerting capabilities that provide visibility into data quality, system health, and operational performance.
• Build dashboards, tooling, and self-service capabilities that improve transparency, efficiency, and decision-making across data operations.
• Partner with Product, Engineering, and Data teams to evolve the infrastructure and platforms supporting AI-enabled products and workflows.
• Identify bottlenecks and opportunities for automation, delivering scalable solutions that improve reliability, consistency, and operational efficiency.

You’ll need to have:

• Bachelor’s degree in Finance, Business, Economics, Accounting, STEM or degree-equivalent qualifications
• 3+ years in data engineering (Python, SQL)
• Experience building ETL/data pipelines at scale and creating data collection frameworks for structured and unstructured data
• Experience with data modeling and developing proactive data quality strategies that ensure data is fit for purpose
• Experience working with ML/AI datasets or experimentation workflows.
• Excellent problem-solving and analytical thinking skills with strong attention to detail.
• Proven track record of stakeholder relationship management, communication, and cross-team collaboration.

We’d love to see:

• Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI.
• Experience in semantic structures or large scale data modeling
• Experience using data visualization tools such as Tableau, QlikSense, or PowerBI
• Experience developing or managing annotation programs and training/evaluation datasets for ML or NLP models.
• Deep domain expertise in financial markets/news and understanding of our customers' needs.

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!