Senior Data Management Professional - Data Quality - Dividend Forecasting
Bloomberg
Data Science, Quality Assurance
Posted on Apr 23, 2026
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 workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
The Team
The Dividend Forecasting Data team monitors and interprets major company developments to identify themes or trends affecting key market sectors. The team is responsible for providing value beyond reported data using their financial knowledge to forecast key data-points about a company that clients can use to understand the potential direction and magnitude of a company’s dividend payout. The team uses their sector and regional domain expertise to generate insightful content for client consumption.
What’s the role?
We are looking for a candidate to help build out effective data management solutions and promote practices to define, measure and manage data quality of our datasets. You will think strategically, apply data management standard methodologies to the data sets, and help us deliver quality data that is ready to use and fit for purpose. To build and maintain your domain expertise, you will be actively involved in the projection process, gaining a deep understanding of how it all works.
As a Senior Data Management Professional, you will work with different teams solving problems and devising solutions for data quality challenges. With that, you would be expected to navigate through unknowns and ambiguity while driving decisions and solutions.
We’ll trust you to:
- Transform how we manage the quality of our datasets, by applying industry best practices to devise quality checks and quality metrics in the ETL processes that create, transform and store data, and measure data against standards defined by our clients’ needs
- Identify and advocating for opportunities to improve the quality of data, through process improvements or workflow infrastructure enhancements
- Provide guidance on how to implement processes to measure, monitor and report on data quality to our internal partners in Product or Sales
- Develop, refine, and deliver the strategy for how to achieve best-in-class data quality, and champion organizational change around data quality as a domain of data management
- Ensure our data is fit-for-purpose and ready-to-use by proactively reviewing the depth, timeliness and accuracy of dataset against user`s expectations
- Perform data profiling and apply statistical methods to support data quality measurements
- Be responsive, resourceful, flexible and an excellent collaborator - Partner with our Product, Technology and Data Management Lab to ensure consistent principles are leveraged, data quality tools are fit for purpose, and results will be measurable
- Keep up with the industry trends, standards, and innovation in the Data Quality domain
You’ll need to have:
- 4+ years of professional experience in data quality management, and/or establishing data governance metrics within Finance or Technology industries
- 4+ years of experience in crafting and developing data quality metrics and reporting using tools such as QlikSense and PowerBI
- Understanding of ETL processes and broader data workflow engineering concepts
- Up-to-date knowledge of events taking place in the financial markets
- Demonstrable experience in Data Profiling/Analysis using tools such as Python, R, or SQL
- Ability to lead multiple projects with global scope in parallel, with superb communication and stakeholder management skills
We’d love to see:
- DAMA CDMP or DCAM certifications
- Familiarity with Corporate Actions, Equity Analysis, Portfolio Management, Dividend Futures, or Listed Derivatives asset classes
- Project Management experience developed in a matrixed partner environment and cross-regional business
- Experience of using Data Science to solve problems practically
- Managing and maintaining data pipelines to ensure reliable, high-quality data flow