Senior Data Management Professional – Metadata, Data Engineering & Automation - Knowledge Graph Group

Our Team

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 and 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 workflow efficiencies and implement technology solutions that enhance our systems, products and processes.

We are building a new Data capability in Dublin focused on metadata management, data quality, semantic modeling and workflow automation. The team will work closely with Bloomberg’s Knowledge Graph, Unified Field Management, Enterprise Data and Data License teams to improve how Bloomberg data is modeled, described, governed and delivered.

Our work spans metadata platforms and products including UFM, XDM, BQL, Calc_Rt, Data License and Bloomberg’s broader semantic and knowledge-graph ecosystem. The goal is not simply to operate existing processes. We want to understand how the work functions today, identify recurring quality and operational problems, and build scalable solutions that improve efficiency, consistency and data quality.

What’s the role?

As a Senior Data Management Professional in Dublin, you will work at the intersection of data management, data engineering, metadata, data quality and semantic modeling.

You will initially develop hands-on expertise across Bloomberg’s metadata and data-management ecosystem, learning how field metadata, APIs, models, datasets and Enterprise products are created, governed and consumed. You will work directly on real metadata, quality and onboarding workflows so that you understand their business purpose, dependencies and operational constraints.

Automation starts with understanding the work: you will first learn the workflow, business rules, dependencies and exceptions before deciding what should be standardized, redesigned or automated.

As your expertise grows, the role will shift toward ownership and improvement. You will take responsibility for workflows and projects, identify systemic quality issues, automate repetitive processes, improve controls and validation, and partner with Product and Engineering on more scalable approaches.

This is deliberately a hybrid role. We are interested in people who may come from data management, data engineering, metadata, data quality or adjacent technical disciplines, provided they are comfortable moving between data analysis, modeling, automation, operational problem-solving and stakeholder engagement.

We’ll trust you to:

• Develop working expertise across UFM, XDM, BQL, Calc_Rt, Data License and related metadata and semantic platforms.
• Own increasingly complex metadata, data-quality and dataset-onboarding work from requirements through validation, release and post-release remediation.
• Analyze data and metadata inventories to identify inconsistencies, gaps, dependencies and opportunities for standardization.
• Investigate data and metadata quality issues using profiling, validation, root-cause analysis and structured data analysis.
• Identify repetitive manual workflows and design automation ranging from lightweight scripts and validation tooling to reusable workflow tooling and integrations, choosing the appropriate technical approach for the problem.
• Build or improve validation, monitoring, reporting and change-impact processes that make data and metadata quality measurable.
• Align related concepts, classifications, enumerations and field definitions across metadata systems and contribute to semantic-modeling and metadata-alignment initiatives.
• Partner with Data Engineering and Engineering teams on tooling, services, APIs and workflow improvements.
• Work across Product, Data and Engineering teams to translate operational needs into metadata, quality controls and scalable technical solutions.
• Evaluate where AI-assisted or agent-assisted tooling can safely improve enrichment, classification, validation or operational workflows.
• Measure the impact of process and automation improvements, including quality effects, operational performance and manual effort displaced.
• Progress toward independent ownership of projects and partner relationships and help mentor newer members of the Dublin team as the capability grows.

You’ll need to have:

*Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

• BA/BS degree or higher in a relevant technical, quantitative, information-management or financial discipline, or equivalent professional experience.
• 4+ years of experience across data management, data engineering, metadata management, data quality, data modeling, analytics or a related discipline.
• Hands-on experience working with structured data using Python, SQL or similar technologies.
• Experience investigating data or metadata quality issues through profiling, validation and root-cause analysis.
• Strong understanding of data-management concepts such as data quality, metadata, data modeling and data lifecycle management.
• Strong analytical judgment, including the ability to understand complex data models, identify dependencies and work through ambiguous problems.
• Experience building automation, analytical tooling, validation processes or other technical solutions to improve data workflows.
• Ability to work effectively across Product, Data and Engineering teams.
• Strong communication skills and the ability to explain technical and data-management concepts to different audiences.

We’d love to see:

• Experience working with financial datasets, financial markets or large-scale data platforms.
• Familiarity with Bloomberg Terminal, Bloomberg APIs such as BQL or Calc_Rt, XDM or Enterprise Data products.
• Experience with metadata registries, data catalogs, semantic layers, controlled vocabularies, taxonomies or ontologies.
• Familiarity with graph and semantic technologies such as RDF, OWL, SKOS, SHACL or GraphQL.
• Experience developing automated validation, monitoring, regression-testing, data-orchestration or governed production-change workflows.
• Experience applying AI, machine learning or LLM-based techniques to metadata enrichment, classification, validation or workflow automation.

Does this sound like you?

Apply if you think we're a good match. We'll get in touch to let you know what the next steps are!

If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.

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