Senior Data Management Professional - Data Engineering - Carbon Data

Avature
London
3 days ago
Create job alert

Senior Data Management Professional - Data Engineering - Carbon Data

Location: London

Business Area: Data

Ref #: 10042873

Description & Requirements

Bloomberg runs on data. Our products are fuelled 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.

Our Team:

Carbon-related Data has become increasingly crucial for our clients, providing deeper insights into risks and opportunities for corporates and financial players on the global path to net-zero. Our team is responsible for delivering high-quality data and insights on carbon markets, including emissions, carbon credits, renewable energy credits and other related content for the Bloomberg Terminal and Enterprise products. Our mission is to provide fit for purpose data to our clients, that empowers them to make well-informed business decisions about carbon pricing, risk management, sustainability and beyond.

The Role:

You're the type of person who looks beyond the headline with a keen eye for detail because you know how data inspires market trends, breaking news, and analysis. With ever increasing coverage and demand for timely data, you will be responsible for managing priorities of different initiatives and for utilizing your subject matter expertise to help drive decisions and generate new dataset offerings. You will need to know your customer, their challenges, and their workflows. With this insight, you will bring creative ideas to the table on how we can enhance our data offering, continuously working to make the integration seamless and more efficient.

We have an exciting opportunity for a highly motivated individual to join our expanding Carbon Data Team, based in our London office. As a Data Engineer, you will use your market knowledge and data management skills to support business decisions and drive growth in the carbon markets domain. You will collaborate closely with other departments such as Product and Engineering and play a key role in empowering our clients to make well-informed business decisions.

We’ll trust you to:

  1. Develop, scale and maintain the data pipelines that interact with our product and databases for the carbon data product.
  2. Design, implement and maintain processes to ensure high quality of carbon data.
  3. Collaborate with cross-functional teams, including Engineering, Product and Sales to drive strategic product development and execution.
  4. Utilize statistics and data visualization to provide meaningful insights into ongoing processes and projects, communicate the results effectively.
  5. Apply your proven project management expertise to ensure technical projects are aligned with requirements and stay on track.
  6. Stay informed on market developments in the offset and allowance carbon markets, leveraging your expertise to enhance client support and generate high-value insights and content.

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.

  1. Bachelor’s degree or higher in a STEM, a relevant data technology field or sustainability field or equivalent professional work experience.
  2. 3+ years of programming experience in a development or production environment.
  3. Demonstrable experience in Data Profiling/Analysis using tools such as Python (Pandas) or SQL.
  4. Strong communication and interpersonal skills, with the ability to convey complex technical concepts to diverse audiences.
  5. Experience in data quality management that ensures data is ready to use, for example, developing proactive data quality strategies, crafting metrics to measure data quality and reporting across organizations.
  6. Strong interest in problem solving particularly to modify and improve processes and workflows.
  7. Effective project management skills and ability to prioritize tasks accordingly.
  8. Demonstrated continuous career growth within an organization.

We’d love to see:

  1. An advanced degree/masters/PhD in a STEM subject of Economics/Finance/Sustainability/Environmental Science.
  2. Demonstrated experience working with carbon markets and products, or other financial markets.
  3. Experience working with SQL or NoSQL databases, including data modelling.
  4. Demonstrable experience in Data Profiling/Analysis using tools such as Python (Pandas) or SQL.
  5. Proven ability to define and implement data quality metrics as a part of a bigger data architecture framework, understanding use cases, competitive offerings and client expectations.
  6. Understanding of large-scale, distributed, end-to-end systems.
  7. Exposure to the Bloomberg Terminal and/or Enterprise data products.
  8. Hands-on project management experience with familiarity in JIRA and QlikSense.
  9. Experience in semantic modelling to enhance data operability across carbon, commodities and ESG reporting datasets.

If this sounds 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, but in the meantime feel free to have a look at this:https://www.bloomberg.com/professional

#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Science Director, London

Senior Data Engineering Consultant

(Only 24h Left) Senior Data Scientist

Drive Data Analyst

Lead Data Engineer

Finance Data Analyst

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.