Senior Data Management Professional - Data Engineer - Private Deals - Bloomberg

Jobs via eFinancialCareers
London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Management Professional - Data Engineer - Capital Structure

Senior Data Analyst: Data Quality, SQL & Reporting

Senior Data Engineer - Azure Data & Analytics (Hybrid)

Senior Data Engineer (Data Management)

Senior Data Engineer (Data Management)

Senior Data Analyst

Senior Data Management Professional - Data Engineer - Private Deals

Location

London

Business Area

Data

Ref #

10047915

Description & Requirements

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint a complete 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 product thinking, domain expertise, and technical insight to continuously improve our data offerings, ensuring they remain reliable, scalable, and fit-for-purpose in a fast-changing landscape.

Our Team:

The Private Deals Data team is responsible for Bloomberg's private transactions data model-an essential foundation for understanding global private markets. This data defines and enriches private companies, linking them to. Private company data plays a critical role in research, investment analysis, compliance, and due diligence. Integrating diverse datasets with our core private company data helps clients uncover insights, validate exposures, monitor evolving risks, and identify new opportunities in opaque and fast-growing markets.

The Role:

We are seeking a Data Engineer to design, build, and maintain the data pipelines, models, and integrations that power Bloomberg's private company data ecosystem. This role focuses on embedding M&A and Private Market data into Bloomberg's semantic model and analytical frameworks such as BQL-enabling richer discoverability and seamless integration across client workflows. You will develop robust ETL processes, manage schema mappings, and implement scalable data transformations to ensure consistency and reliability. You'll also contribute to enhancing Bloomberg's private company valuation product by extending the data model, implementing versioning, and ensuring data provenance is fully traceable. Working closely with Product, Engineering, and Data teams, you'll play a central role in shaping the technical foundation that underpins Bloomberg's evolving private market offerings.

You Will:

  • Design, build, and maintain data pipelines and models to integrate M&A and Private data into Bloomberg's semantic model and BQL.
  • Perform schema mapping, normalization, and transformation to align diverse datasets with internal standards.
  • Implement scalable ETL processes ensuring completeness, accuracy, and transparency of data.
  • Extend the valuation data model to support company-level valuations, versioning, and auditability.
  • Partner with Product and Data teams to define and implement data quality and consistency checks.
  • Develop documentation and reusable frameworks for data ingestion and integration.
  • Support discoverability and search capabilities by ensuring data is optimized for BQL and function-layer exposure.
  • Contribute to continuous improvement of data engineering practices and tools.


You'll Need to Have:

  • We use years of experience as a guide but will consider all candidates who can demonstrate the


Required Skills.

  • 3+ years of experience in data engineering, software engineering, or related fields.
  • Strong programming skills in Python, Java, or Scala, with proficiency in data processing frameworks (e.g., Spark, Flink, or Beam).
  • Solid understanding of data modeling, schema design, and ETL architecture.
  • Experience working with relational and columnar data stores (e.g., SQL, Postgres,BigQuery).
  • Familiarity with semantic data models or knowledge graphs.
  • Understanding of data versioning, provenance tracking, and metadata management.
  • Strong problem-solving and debugging skills with attention to scalability and performance.
  • Comfort working in collaborative, cross-functional environments.


We'd Love to See:

  • Experience integrating financial or private market data.
  • Familiarity with Bloomberg Query Language (BQL) or similar query interfaces.
  • Understanding of valuation data, company financials, or transaction modeling.
  • Experience working in Agile teams.
  • Passion for building high-quality, transparent, and auditable data systems.


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!

Discover what makes Bloomberg unique - watch our for an inside look at our culture, values, and the people behind our success.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.