Senior Data Management Professional - Data Engineering (Data AI)

Bloomberg•New York, NY

1d•$110,000 - $190,000

About The Position

As a Data Engineer within Bloomberg’s Data AI team, 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. Data AI contributes to Bloomberg’s AI-enhanced products by curating model-training data and improving how internal processes use AI. The team provides evaluation and annotation frameworks that connect natural language processing with human judgment, improving the quality, intelligence, and usability of data that drives Bloomberg products. The role partners with Product, Engineering, and Data teams to evolve infrastructure and platforms that support AI-enabled products and workflows.

Requirements

Bachelor’s degree in Finance, Business, Economics, Accounting, STEM, or degree-equivalent qualifications.
At least 3 years of data engineering experience, including Python and 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 to 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.

Nice To Haves

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 customers’ needs.

Responsibilities

Design, build, and maintain scalable data pipelines supporting 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 to 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 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.