Senior Data Engineer — AI Data Pipelines

Bloomberg L.P.; New York, Northern (NY, KY); Hybrid; USD 110,000 - 190,000; Full time

Benefits offered by this job

401(k) match

Excellent benefits and compensation

Job summary

Bloomberg L.P. is seeking a Data Engineer in New York to build scalable data pipelines, data collection frameworks, and quality processes for AI-driven workflows. You will design and operate dataset management and observability tools to support reliable data and model training.

You will collaborate with product and engineering teams to scale infrastructure for AI-enabled products, ensuring data quality and efficiency across operations.

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
• Experience working with ML/AI datasets or experimentation workflows

Responsibilities

• 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 AI workflows.
• Implement monitoring, observability, and alerting to provide visibility into data quality and system health.
• Build dashboards, tooling, and self-service capabilities that improve transparency and decision-making across data operations.
• Partner with Product, Engineering, and Data teams to evolve infrastructure and platforms supporting AI-enabled products.
• Identify bottlenecks and opportunities for automation to improve reliability and efficiency.

Skills

Data engineering

ML/AI datasets

Problem solving

Education

Bachelor's degree in Finance, Business, Economics, Accounting, STEM

Tools

Python

SQL

Tableau

PowerBI

Job description

Bloomberg L.P. is seeking a Data Engineer in New York to build scalable data pipelines, data collection frameworks, and quality processes for AI-driven workflows. You will design and operate dataset management and observability tools to support reliable data and model training.

You will collaborate with product and engineering teams to scale infrastructure for AI-enabled products, ensuring data quality and efficiency across operations.