Senior Data Management Professional - Data Engineer - Commodities Data

Details

Bloomberg’s Data organization delivers the data, news, and analytics that power its products. The role focuses on building and evolving data solutions for commodities and energy products through scalable data pipelines, workflow modernization, automation, and collaboration with Data, Engineering, Product, and business stakeholders.

This is a senior individual contributor position requiring hands-on development, technical leadership, architectural decision-making, mentorship, and ownership of complex data problems from design through production.

Responsibilities

• Design, build, and maintain scalable, resilient data pipelines and workflows supporting critical commodities datasets.
• Develop production-quality data processing and automation solutions using Python, SQL, and other appropriate technologies.
• Own complex technical initiatives end-to-end, including requirements, solution design, implementation, testing, deployment, and ongoing support.
• Modernize legacy data workflows to reduce technical debt, manual intervention, and operational risk while improving maintainability and performance.
• Design reusable and scalable solutions across datasets and workflows.
• 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 data platform evolution.
• Establish and promote technical standards and best practices for Python development, pipeline design, testing, automation, and maintainability.
• Investigate complex data and production issues, perform root-cause analysis, and implement sustainable solutions.
• Build validation, monitoring, and data quality controls into data pipelines.
• Identify opportunities to improve scalability and operational efficiency through automation and improved technical design.
• Understand how clients consume commodities data and translate business and product requirements into effective technical solutions.
• Collaborate with Data, Engineering, and Product stakeholders to define requirements, evaluate tradeoffs, and deliver technical initiatives.
• Provide technical leadership and mentorship in Python, data engineering, automation, and solution design.
• Influence technical direction and make long-term architectural decisions.
• Evaluate and apply emerging technologies, including artificial intelligence and machine learning, where they can improve data acquisition, processing, automation, or operational efficiency.

Requirements

• 3+ years of experience in data management, data engineering, data quality, data operations, or a related technical discipline.
• Strong hands-on Python development skills, including production-quality automation, data processing, validation, or analytical solutions.
• Strong practical SQL experience and experience 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 from problem definition and design through production implementation.
• Experience with modern data platforms, workflow orchestration, and production data systems.
• Experience building production systems with testing, monitoring, observability, and operational controls.
• Ability to evaluate technical tradeoffs and translate business and data requirements into scalable, maintainable solutions.
• Experience providing technical guidance, mentoring others, and influencing technical decisions or engineering practices.
• Strong organizational, communication, collaboration, and stakeholder-influence skills.

Preferred Qualifications

• Experience with commodities, energy, market data, or trading-related datasets.
• STEM background or experience in technical, quantitative, or data-intensive disciplines.
• Familiarity with DataOps concepts.
• Familiarity with statistical approaches to anomaly detection, dynamic thresholding, or time-series data quality monitoring.
• Experience in a regulated or controlled data environment.
• Exposure to cloud-based data platforms and pipeline monitoring tools.
• Experience supporting automation, controls, or AI/ML-based data solutions within a defined validation framework.

Benefits

Salary range: $110,000–$190,000 USD annually, plus benefits and bonus. Actual compensation may vary based on geographic location, work experience, market conditions, education or training, and skill level.

Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental and vision coverage, short- and long-term disability benefits, a 401(k) match, life insurance, and wellness programs.