Senior Data Engineer — Commodities Data Pipelines

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

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Short and long term disability

401(k) + match

Life insurance

Wellness programs

Job summary

Bloomberg L.P. in Princeton is seeking a senior Data Engineer specializing in commodities data to design and evolve scalable data pipelines and automation.

You will lead complex end-to-end data initiatives, modernize legacy workflows, and collaborate with data, engineering, and product teams to deliver high-quality datasets. The role emphasizes hands-on development with Python/SQL, data quality controls, and the ability to influence technical direction while mentoring others.

Qualifications

• 3+ years experience in data management, data engineering, data quality, data operations, or a related technical discipline.
• Strong hands-on Python development skills, with experience building production-quality automation, data processing, validation, or analytical solutions.
• Strong practical experience with SQL and working with large, complex datasets.
• Significant experience designing, building, and maintaining scalable data pipelines and ETL/ELT workflows across diverse data sources.
• Proven ability to own complex technical initiatives end-to-end and drive them from problem definition and design through production implementation.
• Experience with modern data platforms, workflow orchestration, and production data systems.
• Experience providing technical guidance, mentoring others, and influencing technical decisions or engineering practices.
• Strong organizational skills, with the ability to manage multiple priorities and drive work through to completion.
• Strong communication skills and the ability to influence and collaborate effectively across technical and non-technical stakeholders.

Responsibilities

• Design, build, and maintain scalable, resilient data pipelines and workflows supporting critical commodities datasets.
• Develop robust data processing and automation solutions using Python, SQL, and other appropriate technologies.
• Own complex technical initiatives end-to-end, from requirements and solution design through implementation, testing, deployment, and ongoing support.
• Modernize legacy data workflows, reducing technical debt, manual intervention, and operational risk while improving maintainability and performance.
• Design solutions that can be reused and scaled across datasets and workflows rather than solving similar problems independently.
• Work across the data lifecycle, including acquisition, ingestion, transformation, normalization, enrichment, validation, storage, and distribution.
• Partner with Engineering and platform teams on architecture, workflow orchestration, observability, resiliency, and the evolution of our data platforms.
• Establish and promote technical standards and best practices around Python development, pipeline design, testing, automation, and maintainability.
• Investigate complex data and production issues, perform root-cause analysis, and implement sustainable solutions that prevent recurrence.
• Build appropriate validation, monitoring, and data quality controls into data pipelines to ensure reliable and fit-for-purpose data.
• Identify opportunities to improve scalability and operational efficiency through automation and better technical design.
• Understand how clients consume commodities data and translate business and product requirements into effective technical solutions.
• Partner with stakeholders across Data, Engineering, and Product to define requirements, evaluate tradeoffs, and drive technical initiatives through delivery.

Skills

Python

SQL

Data pipelines

ETL/ELT

Automation

Observability

Job description

Bloomberg L.P. in Princeton is seeking a senior Data Engineer specializing in commodities data to design and evolve scalable data pipelines and automation.

You will lead complex end-to-end data initiatives, modernize legacy workflows, and collaborate with data, engineering, and product teams to deliver high-quality datasets. The role emphasizes hands-on development with Python/SQL, data quality controls, and the ability to influence technical direction while mentoring others.