Senior Data Management Professional - Data Engineering - Money Markets Data

at  Bloomberg

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 - all while providing platinum customer support to our clients.

As a Money Markets Data Engineer you will be responsible for reviewing and enhancing our automated processes, building ETLs and developing quality controls. You will design, implement, and maintain our data infrastructure, ensuring the seamless flow of data from various sources to our databases. You will collaborate closely with engineering, domain experts, and other partners to understand data requirements, develop pipelines, and optimize data workflows for efficient data processing. In addition to that, you will build business intelligence tools to analyze our data, derive insights and trends. We are looking for someone highly motivated who is innovative and resourceful, able to collaborate with different partners, and will mentor junior members of the group who are looking to develop technically.

We’ll Trust You To:

• Develop a good understanding about the issuance techniques, Money Markets workflows and how its data is used and consumed by our clients
• Use technical skills to develop, scale, and maintain the data pipelines and processes that interact with our databases
• Optimally collaborate with a variety of internal partners across Data, Engineering, and Sales on strategic product development and execution
• Become familiar with the Bloomberg tech stack as well as standard methodologies related to data management
• Use statistics and data visualization to generate insights about ongoing operations and projects and communicate the results effectively
• Incorporate machine learning and statistics to detect anomalies and drive quality improvements in areas such as accuracy, completeness, consistency and reliability
• Communicate with impact, ensuring complex information is articulated in a meaningful way to a multifaceted audience including senior executives
• Use technology to tackle problems and optimize current processes in areas such as data quality, data acquisition and workflow automation alongside domain experts and technical account managers
• Work with clients on ad-hoc basis, either in support of questions around the dataset or the technology behind it or as part of collaboration on automation projects