The Bloomberg Global Data Alternative Data team seeks an experienced Data Engineer to design, develop, and deploy foundational data processing systems. In this role you will work with a range of non-financial data sets as well as proprietary and open source technologies. As our first hire your interests and enthusiasm will have a significant impact on the strategy and direction of our team.

In the short run, the primary need of the GDAD team is to develop ETL pipelines. Candidates must have demonstrated experience with modern techniques for data acquisition, extraction, normalization, validation, enrichment, and storage.

In the long run, the GDAD team will expand to include originating alternative data (mainly from government and web sources), creating primary research (mainly through survey methods), and supplementing data products with analytical products (mainly by generating systematic signals and ad hoc analysis to support systematic and discretionary investors, respectively). This role can be in either New York, NY or Princeton, NJ.

We expect you to:

• Have experience with data engineering best practices. Professional experience in a data-oriented role is the best way to demonstrate this.
• Have experience with standard software engineering methodologies. We value code quality and strive to develop reliable, maintainable systems.
• Have strong programming experience. We develop primarily in Python and will expect you to do so as well. You should have experience munging and pipelining data, querying databases, and monitoring and troubleshooting systems.
• Communicate effectively with non-technical partners at all stages of a project, from sourcing ideas and figuring out requirements to explaining methodological decisions and implementation details.
• Acquire subject-matter expertise as needed in order to deliver data engineering projects. For example, you might shadow data specialists to learn about their product and workflows.

We'd love to see any of the following:

• Experience working in a large organization. We take care of messy data from disparate sources and legacy systems with limited documentation.
• Experience with financial data or alternative data, preferably from a quantitative role in investment finance or at an alternative data provider.
• Formal training in statistics, machine learning, or software engineering.

Note that we are unable to sponsor candidates requiring visas.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.