Senior Data Management Professional - Data Engineer - Physical Assets

Senior Data Management Professional - Data Engineer - Physical Assets

Location:London

Business Area:Data

Ref #:10042065

Description & Requirements

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.

The team:
The Physical Assets Data Team maintains databases for physical assets such as renewable and conventional power plants, facilities, and storage projects globally. The team is currently working on a new future-proof data model and workflow that can facilitate and accelerate coverage expansion for integrated use in downstream analysis across our customer groups (including governments, portfolio managers, corporations, equity analysts, etc.). The team also manages the databases powering our iconic DINE terminal function which allows users to discover restaurants worldwide. We track and store data such as restaurant locations, the type of cuisine, ownership relationships, etc. Bloomberg users can also review and rate their dining experiences and see recommendations from colleagues and the larger DINE community.

The Role:
As a Data Engineer on the Physical Assets team, you’re required to understand the data requirements, specify the modeling needs of datasets and use existing techstack solutions for efficient data ingestion workflows and data pipelining. You will implement technical solutions using programming, machine learning, AI, and human-in-the-loop approaches to ensure our data is fit-for-purpose for our clients. You will work closely with our Engineering partners, our Data Product Manager, as well as Product teams, so you need to be able to coordinate with multi-disciplinary and regional teams and have experience in project management and stakeholder engagement. You will need to be comfortable working with large, varied, sophisticated, and often unstructured data sets and demonstrate strong experience in data engineering.

We trust you to:

1. Build database schema and configure ETL software to onboard new data sets.
2. Analyze internal processes to find opportunities for improvement, as well as devise and implement innovative solutions.
3. Build quality data workflows to verify and validate third-party data.
4. Maintain workflow configurations for critical functions such as acquisition, worklist management, and quality control.
5. Contribute to the creation of best practices and guidelines for governance.
6. Partner with Engineering and Product to propose, develop, and implement market-leading solutions for our clients.
7. Contribute to the technical implementation of a new Physical Assets Data Model.
8. Understand customer needs and markets to ensure our data sets are fit-for-purpose and seamlessly integrate with other data products when developing data product strategies.
9. Stay updated on market, industry, and dataset developments related to your area of support.
10. Make well-informed decisions in a fast-paced, ever-changing environment.
11. Report on results of ongoing operations and projects, as required.

You’ll need to have:

1. Understanding and experience of large-scale, distributed systems as well as ETL logics.
2. Strong passion for data and the overall energy transition movement.
3. Demonstrated experience with semantic data modeling.
4. The ability to think creatively and provide out-of-the-box solutions with an eagerness to learn and collaborate.
5. Familiarity with data processing paradigms and associated tools and technologies.
6. Exceptional problem-solving skills, numerical proficiency, and high attention to detail.
7. Excellent written and verbal communication skills, especially when explaining technical processes and solutions to business partners and management.
8. Ability to work independently as well as in a distributed team environment.

We’d love to see:

1. Track record of collaborating with Engineering to promote code to production (BREs or DTPs).
2. Knowledge of Machine Learning frameworks.
3. Experience in conducting technical training and mentoring others.
4. Proficiency and previous experience working with Bloomberg tech stack such as BBDS, BCOS, DFR, BRE, DTPs.
5. Prior experience working with QlikSense (both visualizations and load scripting).

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!

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