Senior Data Management Professional - Data Engineering - Data AI
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 1
Agentic AI @ 3
Communication @ 6
Data Engineering @ 6
Data Modeling @ 4
Data Pipelines @ 4
Data Visualization @ 4
ETL @ 4
Experimentation @ 4
GenAI
Generative AI @ 3
Machine Learning
NLP @ 4
Observability
Python @ 6
SQL @ 6
Tableau @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Bloomberg runs on data. In Data, we deliver data, news, and analytics through innovative technology—quickly and accurately—using problem-solving skills to identify workflow efficiencies and implement technology solutions.
Our Team
Data AI contributes to building Bloomberg’s AI-enhanced products at scale by curating model training data and enhancing how internal processes use AI. The team provides evaluation and annotation frameworks connecting natural language processing and human judgment to elevate the quality, intelligence, and usability of the data that drives Bloomberg’s products.
By investing in AI strategically, the team embeds AI across Data. Internal processes take advantage of new AI technologies to strengthen Data’s role in providing robust domain expertise and influential data artifacts to Bloomberg’s products, ensuring clients have high-quality data and access to new dataset types.
The Role
As a Data Engineer within Data AI, you will build and evolve infrastructure, data pipelines, and operational tooling that power scalable AI and data workflows. You will enable reliable data collection, annotation, training, and evaluation by developing systems that improve data quality, operational visibility, and workflow efficiency. Through automation, observability, and platform engineering, you will help create foundations that allow teams to deliver data and AI products with confidence and at scale.
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 to 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.
Requirements
- 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 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.
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 customers' needs.
Benefits
We offer one of the most comprehensive and generous benefits plans available, including merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) + match, life insurance, and various wellness programs (among others). The company does not provide benefits directly to contingent workers/contractors and interns.