Senior Data Management Professional - Data Engineer - Commodities Data

Bloomberg•Princeton, FL

About The Position

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes. We are seeking a highly experienced, hands-on Data Engineering and automation professional to help build and evolve the data solutions that power Bloomberg’s commodities and energy products. This role will focus on designing scalable data pipelines and workflows, modernizing legacy processes, driving automation, and partnering closely with Data, Engineering, Product, and business stakeholders to solve complex data challenges. This is a senior individual contributor role suited to someone with a strong technical background who can combine hands-on development with broader technical leadership. You will be expected to take ownership of complex data problems from design through production, make sound technical and architectural decisions, and build scalable, maintainable solutions using Python and modern data technologies. In addition to delivering solutions directly, you will provide technical guidance and mentorship to others, help establish engineering best practices, and influence technical direction across the team.

Requirements

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.
Demonstrated experience owning complex technical initiatives end-to-end and driving them through implementation.
Experience building production systems with appropriate 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 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.

Nice To Haves

Experience with commodities, energy, market data, or trading-related datasets.
STEM background or experience working with technical, quantitative, or data-intensive disciplines.
Familiarity with DataOps concepts and how data operations and engineering teams work together to improve reliability and delivery.
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 implementation of automation, controls, or AI/ML-based data solutions within a defined validation framework.

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.
Provide technical leadership and mentorship to team members, helping develop their Python, data engineering, automation, and solution-design capabilities.
Influence technical direction by evaluating approaches, challenging existing designs where appropriate, and helping the team make sound long-term architectural decisions.
Evaluate and apply emerging technologies, including AI and machine learning, where they can meaningfully improve data acquisition, processing, automation, or operational efficiency.