Senior Data Management Professional - Data Engineering (Shared Infrastructure)

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 - all while providing customer support to our clients. 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. We partner 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 our products. Our work amplifies the impact of the Data organization by delivering intelligent data solutions and domain-informed systems that enhance the capabilities and competitiveness of Bloomberg’s offerings.

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
A 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, with an understanding of how to design solutions that can scale across multiple use cases
Ability to operate effectively in ambiguous or evolving environments, translating loosely defined problems into practical, scalable solutions
Proven ability to work cross-functionally with engineering, data, and product stakeholders to deliver solutions that are both technically sound and broadly usable
Strong written and verbal communication skills, including the ability to document systems, define patterns, and explain technical trade-offs clearly

Nice To Haves

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

Responsibilities

Design and build reusable data pipelines, libraries, and workflow components supporting annotation and evaluation workflows that can be adopted across teams rather than one-off solutions for a single use case
Contribute to and integrate with automated evaluation frameworks and LLM-enabled annotation workflows in partnership with AI Engineering teams, creating scalable patterns for data generation, validation, and quality measurement
Collaborate on integrations and automation between data systems and LLM services, ensuring solutions are practical, cost-aware, and aligned with engineering constraints
Implement monitoring and observability patterns that help teams detect data quality issues, workflow failures, and performance bottlenecks, including those specific to LLM-driven workflows
Create reference implementations, templates, and tooling that improve developer experience and make it easier for teams to adopt shared patterns
Identify opportunities to reduce manual effort and fragmentation, and implement scalable automation and shared solutions that deliver value across multiple teams
Partner closely with engineering teams to translate prototypes into production-ready capabilities, contributing to designs that can be reliably deployed and maintained
Work directly with data teams to understand pain points, gather feedback, and drive adoption of shared solutions across the organization