Senior Data Management Professional - Data Engineering (Shared Infrastructure)

at Bloomberg

📍 Princeton, United States

USD 110,000-190,000 per year

SENIOR

✅ On-site

Used Tools & Technologies

PostgreSQL

Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Python @ 7 SQL @ 7 Statistics @ 4 Communication @ 7 Data Engineering @ 4 Customer Support @ 4 LLM @ 4 Observability @ 4 AI @ 4 Data Modeling @ 4 Data Pipelines @ 4

Details

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 - all while providing customer support to our clients.

Team

The Bloomberg Data AI group brings innovative AI technologies into Bloomberg’s Data organization while contributing deep financial domain expertise to the development of AI-powered products. The team partners closely with stakeholders to align AI innovation with Bloomberg’s strategic objectives, focusing on optimizing data workflows and elevating the quality, intelligence, and usability of the data that drives Bloomberg’s products.

Role overview

As a Data Engineer on the Shared Infrastructure team, you will shape the foundation for how data workflows are built, scaled, and operated across the organization. You will design and develop shared components, workflow patterns, and developer-facing systems that enable teams to deliver data pipelines with greater consistency, efficiency, and reliability. You will define and implement reusable libraries, templates, and reference architectures for core workflows (ingestion, transformation, evaluation, annotation), and contribute to emerging capabilities such as automated evaluation and LLM-enabled workflows.

Responsibilities

Design and build reusable data pipelines, libraries, and workflow components supporting annotation and evaluation workflows that can be adopted across teams
Contribute to and integrate with automated evaluation frameworks and LLM-enabled annotation workflows
Collaborate on integrations and automation between data systems and LLM services, ensuring cost-aware and practical solutions
Implement monitoring and observability patterns to detect data quality issues, workflow failures, and performance bottlenecks (including LLM-driven workflows)
Create reference implementations, templates, and tooling to improve developer experience and adoption of shared patterns
Identify opportunities to reduce manual effort and fragmentation through scalable automation and shared solutions
Partner with engineering teams to translate prototypes into production-ready capabilities
Work directly with data teams to gather feedback and drive adoption of shared solutions

Requirements

Strong proficiency in Python and SQL, with experience building data pipelines, automation, and analytics workflows
At least 4+ years of professional experience in data engineering, analytics engineering, workflow automation, or a closely related technical role
Bachelor’s degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree-equivalent qualifications
Experience working with object stores (e.g., S3), relational databases (e.g., Postgres), data modeling, and pipeline orchestration in production or near-production environments
Experience building data validation, monitoring, or observability solutions to ensure data quality and workflow reliability
Experience developing reusable components, libraries, or workflows designed to scale across multiple use cases
Ability to operate effectively in ambiguous or evolving environments and translate loosely defined problems into practical, scalable solutions
Proven cross-functional collaboration with engineering, data, and product stakeholders
Strong written and verbal communication skills, including system documentation and explaining technical trade-offs

Nice to have

Experience with LLM-enabled workflows, annotation pipelines, or AI-driven data processes
Familiarity with evaluation frameworks, dataset quality measurement, or approaches to validating model or data outputs
Experience improving fragmented or manual workflows through standardization, automation, and reusable tooling
Exposure to dataset versioning, workflow instrumentation, and data quality monitoring best practices
Experience building shared tools, internal libraries, or systems used across multiple teams
Experience partnering with engineering teams to scale prototypes into production-ready systems
Familiarity with internal tools such as BBGithub, BCOSv2/BCS, BPaaS, QlikSense, DSP, or similar platforms

Location

Princeton, United States

Compensation

Salary Range: 110,000 - 190,000 USD Annual + Benefits + Bonus

Benefits

The company offers a comprehensive benefits plan and a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) + match, life insurance, and wellness programs. (Company does not provide benefits directly to contingent workers/contractors and interns.)