Bloomberg

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.

Our 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. 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.

What’s the role?

As a Data Engineer on the Shared Infrastructure team, you will play a central role in shaping 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, including data ingestion, transformation, evaluation, and annotation, establishing common standards that reduce fragmentation and accelerate development across a distributed set of teams. In addition, you will contribute to the evolution of emerging capabilities, such as automated evaluation and LLM-enabled workflows, partnering closely with engineering teams to help integrate and scale these approaches within production environments.

This role is critical to advancing a more unified, scalable, and maintainable data ecosystem, shifting the organization from bespoke, one-off solutions toward a coherent, systems-driven approach to data and AI workflow development.

We’ll Trust You To:

- 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

You’ll Need To Have: 

- 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

*Please note: years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.

We’d Love To See: 

- 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