Senior Data Engineer, Commodities Data Pipelines

Bloomberg; Princeton (NJ); On-site; USD 110,000 - 190,000; Full time

Job summary

Bloomberg seeks a Senior Data Management Professional - Data Engineer in Princeton to design and evolve data pipelines powering commodities data and analytics. You will own complex initiatives end-to-end, combine hands-on development with technical leadership, and mentor others to raise engineering standards.

You will partner across Data, Engineering, and Product to deliver scalable, automated data solutions using Python, SQL, and modern data platforms, while modernizing legacy processes and

Qualifications

• 3+ years of experience in data management, data engineering, or related technical discipline.
• Strong hands-on Python development skills for production-quality automation and data processing.
• Strong SQL experience with large, complex datasets.
• Experience designing, building, and maintaining scalable data pipelines and ETL/ELT workflows.
• Experience owning technical initiatives end-to-end and driving production implementations.

Responsibilities

• Design, build, and maintain scalable data pipelines and workflows for commodities datasets.
• Develop robust data processing and automation solutions using Python, SQL, and relevant tools.
• Own complex technical initiatives from requirements to production deployment and support.
• Modernize legacy data workflows to reduce manual intervention and improve performance.
• Provide technical guidance and mentorship to team members; influence engineering practices.

Skills

Python

SQL

Data pipelines

Automation

Mentoring

Tools

Airflow

ETL/ELT frameworks

Job description

Bloomberg seeks a Senior Data Management Professional - Data Engineer in Princeton to design and evolve data pipelines powering commodities data and analytics. You will own complex initiatives end-to-end, combine hands-on development with technical leadership, and mentor others to raise engineering standards.

You will partner across Data, Engineering, and Product to deliver scalable, automated data solutions using Python, SQL, and modern data platforms, while modernizing legacy processes and